Claims: 1. A processor-implemented method for biometric verification, comprising: acquiring, via one or more hardware processors, a first biometric sample, the first biometric sample comprising at least a portion of a first high-resolution image of a biometric modality of a user; performing, via the one or more hardware processors, data augmentation on at least the portion of the first high-resolution image to obtain a set of augmented image portions of the first biometric sample; obtaining a set of first feature vectors corresponding to the set of augmented image portions and the first high resolution image, via the one or more hardware processors; encrypting, using Fully Homomorphic Encryption, each first feature vector of the set of first feature vectors using a public key stored at a first computation device to obtain a set of first encrypted feature vectors, via the one or more hardware processors; obtaining, via the one or more hardware processors, a set of encrypted reduced dimensionality first feature vectors corresponding to the set of encrypted first feature vectors, wherein an encrypted reduced dimensionality first feature vector from amongst the set of encrypted reduced dimensionality first feature vectors corresponding to an encrypted first feature vector from amongst the set of encrypted first feature vector is obtained by performing a homomorphic operation of the encrypted feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption and, and wherein the encrypted random projection matrix is pre-assigned to the user and is encrypted using the public key; sharing, via the one or more hardware processors, the set of encrypted reduced dimensionality first feature vectors with a second computation device, wherein the set of encrypted reduced dimensionality feature vectors are compared with a pre-stored homomorphic encrypted second feature vector corresponding to a second biometric sample associated with the user to obtain a set of matching scores, wherein a matching score between an encrypted reduced dimensionality first feature vector of the set of encrypted reduced dimensionality first feature vectors and the homomorphic encrypted second feature vector is indicative of an extent of matching between the first biometric sample and the second biometric sample, and wherein each matching score of the set of encrypted matching scores is encrypted via homomorphic encryption using the public key; receiving, from the second device, the set of encrypted matching scores and decrypting the set of encrypted matching scores using a private key, via the one or more hardware processors; and verifying the user based on a comparison of the set of matching scores with a predetermined threshold score, via the one or more hardware processors. 2. The method of claim 1, wherein acquiring the first biometric sample comprises: capturing the first high resolution image of at least one biometric modality of the user; and segmenting the first high resolution image to obtain the first biometric sample. 3. The method of claim 1, wherein performing the data augmentation on at least the portion of the first high-resolution image comprises performing one or more operations on at least the portion of the first high-resolution image, the one or more operations comprises randomly zooming, shifting horizontally, shifting vertically, flipping horizontally, randomly rotating, changing brightness, cropping and resizing. 4. The method of claim 1, wherein the pre-stored homomorphic encrypted second feature vector corresponding to the second biometric sample associated with the user is stored by: acquiring the second biometric sample comprising at least the portion of a second high-resolution image of the biometric modality of the user during an enrollment phase; obtaining the second feature vector corresponding to the at least the portion of the second high-resolution image; encrypting, using Fully Homomorphic Encryption, the second feature vector using the public key stored at the first computation device to obtain an encrypted second feature vector; obtaining an encrypted reduced dimensionality second feature vector corresponding to the encrypted second feature vector, wherein the encrypted reduced dimensionality second feature vector is obtained by performing a homomorphic operation of the encrypted second feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption, the encrypted random projection matrix pre-assigned to the user, and wherein the encrypted random projection matrix is encrypted using the public key; and sharing the encrypted reduced dimensionality second feature vector with the second computation device. 5. The method of claim 1, wherein obtaining the matching score between the encrypted reduced dimensionality first feature vector and the homomorphic encrypted second feature vector comprises: packing the encrypted reduced dimensionality first feature vector in a first ciphertext and the homomorphic encrypted second feature vector in a second ciphertext; multiplying, elementwise, the first ciphertext with the second ciphertext to obtain a resultant vector; rotating the resultant vector log(n) times to obtain a plurality of rotated resultant vectors; and computing a sum of the plurality of rotated resultant vectors to obtain an inner product of the encrypted reduced dimensionality first feature vector and the homomorphic encrypted second feature vector, wherein the inner product represents the matching score between the encrypted reduced dimensionality first feature vector and the homomorphic encrypted second feature vector. 6. The method of claim 1, wherein the first high-resolution image is acquired during a verification phase of the biometric verification. 7. The method of claim 1, further comprising sharing, with the second device, the public key, encrypted random projection matrix, and a unique identifier (ID) associated with the user. 8. The method of claim 1, wherein the public key and the private key are associated with the user. 9. The method of claim 1, wherein the first computation device is a client device. 10. The method of claim 1, wherein the second computation device is a server device. 11. A system for biometric verification comprising: one or more memories; and one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions in a trusted execution environment (TEE), the programmed instructions stored in the one or more memories, to: acquire a first biometric sample, the first biometric sample comprising at least a portion of a first high-resolution image of a biometric modality of a user; perform data augmentation on at least the portion of the first high resolution image to obtain a set of augmented image portions of the first biometric sample; obtain a set of first feature vectors corresponding to the set of augmented image portions and the first high resolution image; encrypt, using Fully Homomorphic Encryption, each first feature vector of the set of first feature vectors using a public key stored at a first computation device to obtain a set of first encrypted feature vectors; obtain a set of encrypted reduced dimensionality first feature vectors corresponding to the set of encrypted first feature vectors, wherein an encrypted reduced dimensionality first feature vector from amongst the set of encrypted reduced dimensionality first feature vectors corresponding to an encrypted first feature vector from amongst the set of encrypted first feature vector is obtained by performing a homomorphic operation of the encrypted feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption, and wherein the encrypted random projection matrix is pre-assigned to the user and encrypted using the public key; share the set of encrypted reduced dimensionality first feature vectors with a second computation device, wherein the set of encrypted reduced dimensionality feature vectors are compared with a pre-stored homomorphic encrypted second feature vector corresponding to a second biometric sample associated with the user to obtain a set of matching scores, wherein a matching score between an encrypted reduced dimensionality first feature vector of the set of encrypted reduced dimensionality first feature vectors and the homomorphic encrypted second feature vector is indicative of an extent of matching between the first biometric sample and the second biometric sample, and wherein each matching score of the set of encrypted matching scores is encrypted via homomorphic encryption using the public key; receive, from the second device, the set of encrypted matching scores and decrypting the set of encrypted matching scores using a private key; and verify the user based on a comparison of the set of matching scores with a predetermined threshold score. 12. The system of claim 11, wherein to acquire the first biometric sample, the one or more hardware processors are configured by the instructions to: capture the first high resolution image of at least one biometric modality of the user; and segment the first high resolution image to obtain the first biometric sample. 13. The system of claim 11, wherein to perform the data augmentation on the at least portion of the first high resolution image, the one or more hardware processors are configured by the instructions to perform one or more operations on at least the portion of the first high resolution image, the one or more operations comprises randomly zooming, shifting horizontally, shifting vertically, flipping horizontally, randomly rotating, changing brightness, cropping and resizing. 14. The system of claim 11, wherein to store the pre-stored homomorphic encrypted second feature vector corresponding to the second biometric sample associated with the user, the one or more hardware processors are configured by the instructions to: acquire the second biometric sample comprising at least the portion of a second high-resolution image of the biometric modality of the user during an enrollment phase; obtain the second feature vector corresponding to the at least the portion of the second high-resolution image; encrypt, using Fully Homomorphic Encryption, the second feature vector using the public key stored at the first computation device to obtain an encrypted second feature vector; obtain an encrypted reduced dimensionality second feature vector corresponding to the encrypted second feature vector, wherein the encrypted reduced dimensionality second feature vector is obtained by performing a homomorphic operation of the encrypted second feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption, the encrypted random projection matrix pre-assigned to the user, and wherein the encrypted random projection matrix is encrypted using the public key; and share the encrypted reduced dimensionality second feature vector with the second computation device. 15. The system of claim 11, wherein to obtain the matching score between the encrypted reduced dimensionality first feature vector and the homomorphic encrypted second feature vector, the one or more hardware processors are configured by the instructions to: pack the encrypted reduced dimensionality first feature vector in a first ciphertext and the homomorphic encrypted second feature vector in a second ciphertext; multiply, elementwise, the first ciphertext with the second ciphertext to obtain a resultant vector; rotate the resultant vector log(n) times to obtain a plurality of rotated resultant vectors; and compute a sum of the plurality of rotated resultant vectors to obtain an inner product of the encrypted reduced dimensionality first feature vector and the homomorphic encrypted second feature vector, wherein the inner product represents the matching score between the encrypted reduced dimensionality first feature vector and the homomorphic encrypted second feature vector. 16. The system of claim 11, wherein the first high resolution image is acquired during a verification phase of the biometric verification. 17. The system of claim 11, wherein the one or more hardware processors are further configured by the instructions to share, with the second device, the public key, encrypted random projection matrix, and a unique identifier (ID) associated with the user. 18. The system of claim 11, wherein the public key and the private key are associated with the user. 19. The system of claim 11, wherein the first computation device is a client device. 20. The system of claim 11, wherein the second computation device is a server device. , Description:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: METHOD AND SYSTEM FOR BIOMETRIC VERIFICATION Applicant: Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th Floor, Nariman Point, Mumbai 400021, Maharashtra, India The following specification particularly describes the invention and the manner in which it is to be performed. TECHNICAL FIELD [001] The disclosure herein generally relates to biometric verification, and, more particularly, to system and method for biometric verification using fully homomorphic encryption. BACKGROUND [002] With the growth of the Internet and communication technology, more and more information is being exchanged over the Internet. The information includes, but is not limited to, services, applications, and content. Said information includes personal as well as public information associated with a user. In the scenarios where the information communicated over the Internet is personal information, additional care has to be taken for maintaining trustworthiness of users accessing the information and user devices utilized for communication. For communicating the information in a secure environment, various authentication schemes are utilized for authentication of the users accessing such information. [003] One such authentication scheme utilizes biometric verification of the user. Examples of such biometrics includes, but are not limited to, face, iris, fingerprint, and so on. The growing use of biometrics has, however, led to rising concerns about the security and privacy of biometric data (also referred to as biometric template) since it is unique to each individual and cannot be replaced. SUMMARY [004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor-implemented method for biometric verification is provided. The method includes acquiring, via one or more hardware processors, a first biometric sample, the first biometric sample comprising at least a portion of a first high-resolution image of a biometric modality of the user. Further, the method includes performing, via the one or more hardware processors, data augmentation on the at least portion of the first high resolution image to obtain a set of augmented image portions of the first biometric sample. Furthermore, the method includes obtaining a set of first feature vectors corresponding to the set of augmented image portions and the first high resolution image, via the one or more hardware processors. Also, the method includes encrypting, using Fully Homomorphic Encryption, each first feature vector of the set of first feature vectors using a public key stored at a first computation device to obtain a set of first encrypted feature vectors, via the one or more hardware processors. Moreover, the method includes obtaining, via the one or more hardware processors, a set of encrypted reduced dimensionality first feature vectors corresponding to the set of encrypted first feature vectors, wherein an encrypted reduced dimensionality first feature vector from amongst the set of encrypted reduced dimensionality first feature vectors corresponding to an encrypted first feature vector from amongst the set of encrypted first feature vector is obtained by performing a homomorphic operation of the encrypted feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption, and wherein the encrypted random projection matrix is pre-assigned to the user, and is encrypted using the public key. Still further, the method includes sharing, via the one or more hardware processors, the set of encrypted reduced dimensionality first feature vectors with a second computation device, wherein the set of encrypted reduced dimensionality feature vectors are compared with a pre-stored homomorphic encrypted second feature vector corresponding to a second biometric sample associated with the user to obtain a set of matching scores, wherein a matching score between an encrypted reduced dimensionality first feature vector of the set of encrypted reduced dimensionality first feature vectors and the homomorphic encrypted second feature vector is indicative of an extent of matching between the first biometric sample and the second biometric sample, and wherein each matching score of the set of encrypted matching scores is encrypted via homomorphic encryption using the public key. Also, the method includes receiving, from the second device, the set of encrypted matching scores and decrypting the set of encrypted matching scores using a private key, via the one or more hardware processors. The method further includes verifying the user based on a comparison of the set of matching scores with a predetermined threshold score, via the one or more hardware processors. [005] In another aspect, a system for biometric verification is provided. The system includes one or more memories; and one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions in a trusted execution environment (TEE), the programmed instructions stored in the one or more memories, to acquire a first biometric sample, the first biometric sample comprising at least a portion of a first high-resolution image of a biometric modality of the user. Further, the one or more hardware processors are configured to execute programmed instructions to perform data augmentation on the at least portion of the first high resolution image to obtain a set of augmented image portions of the first biometric sample. Furthermore, the one or more hardware processors are configured to execute programmed instructions obtain a set of first feature vectors corresponding to the set of augmented image portions and the first high resolution image. Moreover, the one or more hardware processors are configured to execute programmed instructions to encrypt, using Fully Homomorphic Encryption, each first feature vector of the set of first feature vectors using a public key stored at a first computation device to obtain a set of first encrypted feature vectors. Also, the one or more hardware processors are configured to execute programmed instructions to obtain a set of encrypted reduced dimensionality first feature vectors corresponding to the set of encrypted first feature vectors, wherein an encrypted reduced dimensionality first feature vector from amongst the set of encrypted reduced dimensionality first feature vectors corresponding to an encrypted first feature vector from amongst the set of encrypted first feature vector is obtained by performing a homomorphic operation of the encrypted feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption, and wherein the encrypted random projection matrix is and pre-assigned to the user and is encrypted using the public key. Also, the one or more hardware processors are configured to execute programmed instructions to share the set of encrypted reduced dimensionality first feature vectors with a second computation device, wherein the set of encrypted reduced dimensionality feature vectors are compared with a pre-stored homomorphic encrypted second feature vector corresponding to a second biometric sample associated with the user to obtain a set of matching scores, wherein a matching score between an encrypted reduced dimensionality first feature vector of the set of encrypted reduced dimensionality first feature vectors and the homomorphic encrypted second feature vector is indicative of an extent of matching between the first biometric sample and the second biometric sample, and wherein each matching score of the set of encrypted matching scores is encrypted via homomorphic encryption using the public key. Also, the one or more hardware processors are configured to execute programmed instructions to receive, from the second device, the set of encrypted matching scores and decrypting the set of encrypted matching scores using a private key. The one or more hardware processors are further configured to execute programmed instructions to verify the user based on a comparison of the set of matching scores with a predetermined threshold score. [006] In yet another aspect, a non-transitory computer readable medium for a method for biometric verification is provided. The method includes acquiring, via one or more hardware processors, a first biometric sample, the first biometric sample comprising at least a portion of a first high-resolution image of a biometric modality of the user. Further, the method includes performing, via the one or more hardware processors, data augmentation on the at least portion of the first high resolution image to obtain a set of augmented image portions of the first biometric sample. Furthermore, the method includes obtaining a set of first feature vectors corresponding to the set of augmented image portions, via the one or more hardware processors. Also, the method includes encrypting, using Fully Homomorphic Encryption, each first feature vector of the set of first feature vectors using a public key stored at a first computation device to obtain a set of first encrypted feature vectors, via the one or more hardware processors. Moreover, the method includes obtaining, via the one or more hardware processors, a set of encrypted reduced dimensionality first feature vectors corresponding to the set of encrypted first feature vectors, wherein an encrypted reduced dimensionality first feature vector from amongst the set of encrypted reduced dimensionality first feature vectors corresponding to an encrypted first feature vector from amongst the set of encrypted first feature vector is obtained by performing a homomorphic operation of the encrypted feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption, and wherein the encrypted random projection matrix is pre-assigned to the user and is encrypted using the public key. Still further, the method includes sharing, via the one or more hardware processors, the set of encrypted reduced dimensionality first feature vectors with a second computation device, wherein the set of encrypted reduced dimensionality feature vectors are compared with a pre-stored homomorphic encrypted second feature vector corresponding to a second biometric sample associated with the user to obtain a set of matching scores, wherein a matching score between an encrypted reduced dimensionality first feature vector of the set of encrypted reduced dimensionality first feature vectors and the homomorphic encrypted second feature vector is indicative of an extent of matching between the first biometric sample and the second biometric sample, and wherein each matching score of the set of encrypted matching scores is encrypted via homomorphic encryption using the public key. Also, the method includes receiving, from the second device, the set of encrypted matching scores and decrypting the set of encrypted matching scores using a private key, via the one or more hardware processors. The method further includes verifying the user based on a comparison of the set of matching scores with a predetermined threshold score, via the one or more hardware processors. [007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS [008] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: [009] FIG. 1 illustrates an example network implementation of a system for biometric verification in accordance with an example embodiment of the present disclosure. [010] FIG. 2 illustrates a flow diagram for a method for biometric verification in accordance with an example embodiment of the present disclosure. [011] FIG. 3 illustrates an example process flow for a method of biometric verification in an example embodiment of the present disclosure. [012] FIG. 4 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS [013] The term ‘biometrics’ is defined as automated recognition of individuals based on their unique behavioral and biological characteristics (ISO/IEC JTC1 SC37). A typical biometric system obtains said unique behavioral and physical characteristics by acquiring the user’s biometric trait (such as fingerprints, iris, face, voice, gait, and so on) via one or more sensors. Acquired biometric data is processed to extract the salient information (feature set). During enrollment phase, the extracted feature set is stored in the database as a template. During verification, a matcher module accepts two biometric templates, namely a stored template and a query template as inputs and outputs a matching score indicating the similarity between the two templates. If the matching score exceeds a certain threshold the user is verified successfully. [014] A secure biometric system should not only accurately authenticate the user (less false rejects) and deny access to imposters (less false accepts), it should also store the templates in a secure manner. This is important because unlike credit cards and passwords which when compromised can be revoked and reissued, biometric data (template) is permanently associated with the user and cannot be replaced. If a biometric template is exposed once, it is lost forever. Further, a compromised biometric template can be misused for cross-matching across databases. Therefore, biometric template protection is an important issue in designing a secure biometric system. [015] The conventional biometric verification systems pose a challenge with respect to intra-user variability that is caused by changes in user’s pose, illumination, expression, and so on. Moreover, such systems assume that the input to the system is an image of, for example, a face in which the positions of the eyes and lips are known. However, for many real-world applications, such assumptions are unrealistic and impractical. Certain known biometric verification system utilizes Partial Homomorphic Encryption with support for binarized data for encryption purposes. Such systems require quantification of feature vectors. The quantization of feature vectors leads to loss of information (or lossy computations) thereby leading to low matching performance during verification of the biometric information. [016] Typical biometric verifications systems perform computations in non-encrypted domain. For example, in such conventional system, a feature vector associated with the biometric sample is mapped to a randomly generated binary code. The cryptographic hash of the binary code represents the protected biometric template. Moreover, in another known system, similarity computation between the stored template and the query biometric templates is performed in an unencrypted domain. Since said computations are performed in unencrypted domain, such computations pose, both security and privacy concern for the verification process. [017] Various embodiments disclosed herein provide method and system for biometric verification in a secure manner using fully homomorphic encryption. For example, in various embodiments, the disclosed system utilizes homomorphic encryption computations for encrypting the feature vectors of the stored biometric template and the query biometric template. Moreover, the disclosed system performs dimensionality reduction in the encrypted domain using homomorphic encryption. In addition, the disclosed system makes use of an encrypted random projection matrix as a security feature. An important contribution of the disclosed embodiments is that unlike conventional systems, the disclosed embodiments do not require quantification of feature vectors. Instead, the disclosed embodiments perform computations with real valued feature vectors, thereby precluding need for lossy computations performed in conventional systems. [018] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims. [019] Referring now to the drawings, and more particularly to FIG. 1 through 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. [020] FIG. 1 illustrates a network implementation 100 of a system (for example a system 102a and/or a system 102b) for biometric verification, in accordance with example embodiments. In one embodiment, the network implementation 100 includes at least one first device, for example a first device 104 and at least one second device, for example a second device 106. In an embodiment, the first device 104 may be a client device. In an embodiment, the second device 106 may be a server device. Herein, it will be understood that the system, for example the system 102a/102b may be embodied in or communicatively coupled to the first device 104 and the second device 106. For example, as illustrated in FIG. 1, the system 102a is shown to be communicatively coupled to the first device 104 and the system 102b is shown to be communicatively coupled to the second device 106. Alternatively, one or both of the systems 102a, 102b can be implemented in a computation device outside of the first device and the second device respectively, and communicatively couple thereto. Hereinafter, the systems 102a, 102b may be collectively referred to as a system 102. [021] The first device 104 and the second device 106 may be communicably coupled to each other through a communication network 108. It will be noted herein that the number of devices and/or networks, illustrated in FIG. 1, is provided for explanatory purposes only. In practice or some example scenarios, there may be additional or fewer devices and/or networks; different devices and/or networks; or differently arranged devices and/or networks than illustrated in FIG. 1. Also, in some implementations, one or more of the devices of environment 100 may perform one or more functions described as being performed by another one or more of the devices of environment 100. Devices and/or networks of environment 100 may interconnect via wired connections, wireless connections (laser, infrared, RF, optical), or a combination of wired and wireless connections over the communication network 108. [022] The communication network 108 may be a wireless network, wired network or a combination thereof. The communication network 108 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, a metropolitan area network (MAN), an ad hoc network, an intranet, a fiber optic-based network, and/or a combination of these or other types of networks. Additionally or alternatively, the communication network 108 may include a cellular network, the Public Land Mobile Network (PLMN), a second generation (2G) network, a third generation (3G) network, a fourth generation (4G) network (e.g., a long term evolution (LTE) network), a fifth generation (5G) network, and/or another network. The communication network 108 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further the communication network 108 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. [023] The network environment 100 supports various connectivity options such as BLUETOOTH®, USB, ZigBee and other cellular services. The network environment enables connection of devices 106 such as Smartphone with the server 104, and accordingly with the database 112 using any communication link including Internet, WAN, MAN, and so on. In an exemplary embodiment, the system 102 is implemented to operate as a stand-alone device. In another embodiment, the system 102 may be implemented to work as a loosely coupled device to a smart computing environment. [024] The first device 104 may include any computation or communication device that is capable of communicating via the communication network 108. For example, the client device may be a computation device that may be capable of facilitating a user access to a service requiring user biometric verification. In another example, the client device 104 may be implemented in a variety of communication devices such as a laptop computer, a desktop computer, a notebook, a workstation, a mobile phone, a personal digital assistant (PDA), and so on. The client device is configured to enroll and verify a user, for example, for accessing a service or premises. For example, in an enrollment phase, the client device may allow the user to enroll by using encrypted biometric information thereof. Post enrolment, i.e. during verification phase, the user’s assess can be verified using the enrolled encrypted biometric information. [025] The second device 104 may include one or more server devices, or other types of computation and communication devices that may receive encrypted biometric information during the enrollment phase and the verification phase from the client device. The sever may determine an extent of match between the encrypted biometric information stored during the enrollment phase with the encrypted biometric information received during the verification phase to compute matching scores. The server shares said matching scores with the client device for biometric verification of the user at the client device. [026] In an embodiment, the system, for example the systems 102a, 102b may be implemented in a computing device, for instance the computing devices 104, 106 such as a hand-held device, a laptop or other portable computer, a tablet computer, a mobile phone, a PDA, a smartphone, and a desktop computer. The system 102 may also be implemented in a workstation, a mainframe computer, a server, and a network server. In an embodiment, the system 102a, 102b may be coupled to a data repository, for example, a repository 112a, 112b respectively (hereinafter referred to as a repository or database 112). The repository 112 may store data processed, received, and generated by the system 102. In an alternate embodiment, the system 102 may embody the data repository 112. The components and functionalities of the system 102 are described further in detail with reference to FIGS. 2-4. [027] Referring collectively to FIGS. 2-4, components and functionalities of the system 102 for biometric verification are described in accordance with an example embodiment. For example, FIG. 2 illustrates a flow diagram for a method for biometric verification in accordance with an example embodiment. FIG. 3 illustrates a process flow diagram of a system for biometric verification, in accordance with an example embodiment. FIG. 4 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. [028] For the purpose of biometric verification, the proposed method includes two phases, namely an enrollment phase and a verification phase. During each of the enrollment and the verification phases, one or more biometric samples are acquired from the user at the first device. Examples of such biometric samples may include, but are not limited to, images of user’s face, retina, fingerprints, palm print, and finger knuckle. [029] Hereinafter, the biometric sample acquired during the verification phase may be referred to as a first biometric sample and the biometric sample acquired during the enrolment phase may be termed as second biometric sample. [030] The first biometric sample and the second biometric sample may be obtained by capturing a high-resolution image of the user and thereafter preprocessing the biometric sample from the captured high-resolution image. Hereinafter, the high-resolution image acquired during the verification phase may be referred to as a first high-resolution image and the high-resolution image acquired during the enrolment phase may be termed as second high-resolution image. In various embodiments, the method includes capturing one shot enrollment such that a single image of the user’s biometric modality captured and used for enrollment. The single image may be a high-resolution image that may enable capturing of biometric details for the purpose of enrolment. In an embodiment, the client device may include an image sensor for retrieving the high-resolution image of the user in single shot enrolment. [031] The high-resolution image, for example, the second high-resolution image, captured during the enrolment phase is preprocessed to detect the biometric modality in said image. In an embodiment, for acquiring the second biometric sample, the system 102 may first retrieve the biometric modality in the captured high-resolution image and segment at least a portion of the biometric modality from it to obtain a segmented image portion. In an embodiment, the segmented image portion may be represented by “x*x” image. Herein, a system, for example, the system 102a may facilitate in detecting the biometric modality in the captured high-resolution image. For instance, the second high-resolution image may include an upper torso of the user including users head, face, and shoulders. However, for the purpose of the biometric verification only the face image or the eye image may be required. In such a scenario, during the pre-processing stage, the second biometric sample of the user containing the biometric modality such as the face or the iris may be acquired from the second high-resolution image by first detecting the second biometric sample in the second high-resolution image and thereafter segmenting the detected iris from the second high-resolution image. In an embodiment, a detector model, for example, a Multi-Task Cascaded Convolutional Networks (MTCNN) may be utilized for detecting the second biometric sample from the captured second high-resolution image. [032] Upon acquiring the second biometric sample, a second feature vector corresponding to the at least the portion of the second high-resolution image is obtained. For example, the segmented “x*x” image may be given as an input to a feature detection model. The feature detection model may be a pre-trained model for feature vector extraction. The feature detection model may output a discriminative feature vector, i.e. the second feature vector corresponding to the segmented image of dimension “x*x”. Herein the discriminative feature vector may capture the uniqueness / unique features present in the portion of the second high-resolution image. The second feature vector may be encrypted using a public key stored at the first computation device to obtain an encrypted second feature vector. Herein, a key pair having a public key and a private key may be generated at the client device. Said generated key pair may be utilized during the biometric verification of the user. [033] In an embodiment, a homomorphic operation (for example, a multiplication operation) of the encrypted second feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption is performed to obtain an encrypted reduced dimensionality second feature vector corresponding to the encrypted second feature vector. Since the feature vector is encrypted using Fully Homomorphic Encryption (FHE) which is computationally intensive, therefore dimensionality reduction is of prime importance. In an embodiment, the dimensionality of the encrypted second feature vector may be reduced by using an encrypted random project matrix pre-assigned to the user. The random projection matrix may be encrypted using a Fully Homomorphic Encryption. In an example, a homomorphic operation of the encrypted second feature vector with an encrypted random projection matrix encrypted using Fully Homomorphic Encryption is performed to obtain an encrypted reduced dimensionality second feature vector. In an embodiment, the encrypted random projection matrix is encrypted using the public key of key pair assigned to the user. Herein, it will be understood that the random projection matrix is independent of the user and the user’s biometric modality. The random projection matrix serves primarily two purposes. Firstly, the random projection matrix reduces the dimensionality of the feature vector. Secondly, the random projection matrix may act as a cancellable transform and thus provides an additional security layer. In case the user’s encrypted feature vector stored on the server database is compromised, the user can generate new key pair (using the key generation) and can also generate a new random projection matrix. The random projection matrix of dimension ‘k*d’ is used to project the original ‘d’ dimensional data to a ‘k’ dimensional (k<