1. A gender data classification system using deep learning comprising: a set of sensors for providing data to a deep learning model; a database for capturing correspondences of faces by face tracking; a data log unit operatively coupled to the one or more processing unit; a storage unit configured to store machine-readable instruction; a processor configured to classifying the gender data using the deep learning model and the one or more classifiers; and a communication module configured to transmit data, wirelessly, to a central computer.

2. The gender data classification system using deep learning as claimed in claim 1, wherein the memory coupled to the one or more processors, the storage unit having stored instructions which executed by the one or more processors.

3. The gender data classification system using deep learning as claimed in claim 1, wherein the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data.

4. The gender data classification system using deep learning as claimed in claim 1, wherein the training a fast learning model based on at least the one or more modified classifiers and that portion of the data that corresponds to the one or more classification errors.

5. The gender data classification system using deep learning as claimed in claim 1, wherein the database selected from the group consisting of a Floppy Disk, a DVD, a Blu-Ray Disk, a CD, a ROM, a PROM, an EPROM, an EEPROM or a flash memory, a hard Disk, or another magnetic or optical memory, on which electronically readable control signals are stored.

6. The gender data classification system using deep learning as claimed in claim 1, wherein the communication module via a wireless transmission channel employing at least one selected from the group consisting of Wi-Fi module, cellular communication module, Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX) module, Wireless Broadband (WiBro) module, and High-Speed Downlink Packet Access (HSDPA) module.

TECHNICAL FIELD
[0001] The invention relates to the field of a gender recognition, and more specifically to a gender classification system with a deep learning neural network.

BACKGROUND ART
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Gender recognition is important as it can boost the performance of many applications, including person recognition and human-computer interfaces.
[0004] Face detection is the essential first step for almost all face information processing systems. Progress in face detection makes it possible for surveillance systems to take a human face as an input pattern and extract information from it.
[0005] Currently, the main methods used in gender classification are Neural Network and Support Vector Machine (SVM).
[0006] Automatic gender recognition using images has a wide range of applications such as security, marketing, and computer user interface. Online applications, such as computer user interface, or gender targeted advertisements, especially demand highly accurate gender recognition capabilities.
[0007] Traditional gender recognition methods make use of holistic facial appearance and/or bodily features that are specific to certain dress codes or ethnic groups. The use of holistic facial appearance for gender recognition fits well into the framework of machine learning-based classification, because facial appearance has common structure across the human population to be compared against each other, and at the same time provides useful appearance information to differentiate gender. It is well known in the pattern analysis community that one can achieve higher recognition accuracy when the patterns are aligned more accurately. In general, when the overall patterns are aligned, the learning machine does a better job of identifying the fine-level features necessary for identifying the difference between classes.
[0008] For the gender recognition problem, the manner through which the human brain processes the visual information from a human face to determine gender is not completely understood. However, there are certain features that are known to contribute more to the task of gender recognition; studies revealed that certain parts of the face or facial features provide more decisive image information for gender recognition. For example, there is a general consensus that differences in the size and shape between male eyebrows and female eyebrows exist.
[0009] On the other hand, studies have revealed that using only facial image for gender recognition has limitations; even gender recognition by humans using only facial images is shown to have such limitations. Humans make use of other image cues, such as hairstyles, body shape, and dress codes, for determining the gender.
[0010] Therefore, there a gender classification system with a deep learning neural network. Therefore, the present disclosure overcomes the above-mentioned problem associated with the traditionally available method or system, any of the above-mentioned inventions can be used with the presented disclosed technique with or without modification.
[0011] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0012] The principal object of the present invention is to overcome the disadvantages of the prior art.
[0013] Another object of the present invention is to provide a gender data classification system using deep learning.
[0014] Another object of the present invention is to provide deep learning approach for the identification characteristics derived from the human body's facial data.
[0015] Another object of the present invention is to provide image processing-based features on gender classification.
[0016] Another object of the present invention is to provide an elegant, reliable and precise approach towards the gender data classification system using deep learning.
[0017] Yet another object of the present invention is to provide a process of improving functionalities of the gender data classification system using deep learning
SUMMARY
[0018] The invention relates to the field of a gender recognition, and more specifically to a gender classification system with a deep learning neural network.
[0019] The gender data classification system using deep learning includes a set of sensors for providing data to a deep learning model, a database for capturing correspondences of faces by face tracking, a data log unit operatively coupled to the one or more processing unit, a storage unit configured to store machine-readable instruction, a processor configured to classifying the gender data using the deep learning model and the one or more classifiers, and a communication module configured to transmit data, wirelessly, to a central computer.
[0020] According to an aspect, the memory coupled to the one or more processors, the storage unit having stored instructions which executed by the one or more processors.
[0021] According to an aspect, the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data.
[0022] According to an aspect, the training a fast learning model based on at least the one or more modified classifiers and that portion of the data that corresponds to the one or more classification errors.
[0023] According to an aspect, the database selected from the group consisting of a Floppy Disk, a DVD, a Blu-Ray Disk, a CD, a ROM, a PROM, an EPROM, an EEPROM or a flash memory, a hard Disk, or another magnetic or optical memory, on which electronically readable control signals are stored.
[0024] According to an aspect, the communication module via a wireless transmission channel employing at least one selected from the group consisting of Wi-Fi module, cellular communication module, Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX) module, Wireless Broadband (WiBro) module, and High-Speed Downlink Packet Access (HSDPA) module.
[0025] These and other features will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0026] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0027] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein: Figures attached: N.A.
DETAILED DESCRIPTION OF THE INVENTION
[0028] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0029] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0030] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[0031] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations.
[0032] The invention relates to the field of a gender recognition, and more specifically to a gender classification system with a deep learning neural network.
[0033] The gender data classification system using deep learning includes a set of sensors for providing data to a deep learning model, a database for capturing correspondences of faces by face tracking, a data log unit operatively coupled to the one or more processing unit, a storage unit configured to store machine-readable instruction, a processor configured to classifying the gender data using the deep learning model and the one or more classifiers, and a communication module configured to transmit data, wirelessly, to a central computer.
[0034] According to an aspect, the memory coupled to the one or more processors, the storage unit having stored instructions which executed by the one or more processors.
[0035] According to an aspect, the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data.
[0036] According to an aspect, the training a fast-learning model based on at least the one or more modified classifiers and that portion of the data that corresponds to the one or more classification errors.
[0037] According to an aspect, the database selected from the group consisting of a Floppy Disk, a DVD, a Blu-Ray Disk, a CD, a ROM, a PROM, an EPROM, an EEPROM or a flash memory, a hard Disk, or another magnetic or optical memory, on which electronically readable control signals are stored.
[0038] According to an aspect, the communication module via a wireless transmission channel employing at least one selected from the group consisting of Wi-Fi module, cellular communication module, Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX) module, Wireless Broadband (WiBro) module, and High-Speed Downlink Packet Access (HSDPA) module.
[0039] The invention may be implemented in hardware, firmware or software, or a combination of the three. Preferably the invention is implemented in a computer program executed on a programmable computer having a processor, a data storage system, volatile and non-volatile memory and/or storage elements, at least one input device and at least one output device.
[0040] The memory may be implemented using computer-readable media, such as computer storage media. Computer-readable media includes, at least, two types of computer-readable media, namely computer storage media and communications media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD), high-definition multimedia/data storage disks, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media may embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanisms. The memory may also include a firewall.
[0041] Embodiments of the invention may be implemented in hardware or in software, depending on certain implementation requirements. The implementation can be performed using a digital storage medium (for example, a Floppy Disk, a DVD, a Blu-Ray Disk, a CD, a ROM, a PROM, an EPROM, an EEPROM or a flash memory, a hard Disk, or another magnetic or optical memory, on which electronically readable control signals are stored, which interact or can interact with programmable hardware components such that the corresponding method is performed.
[0042] The Programmable hardware component may be formed by a processor, a Computer Processor (CPU), a Graphics Processing Unit (GPU), a computer program, an Application-Specific Integrated Circuit (ASIC), an Integrated Circuit (IC), a System on Chip (SOC), a Programmable logic device, or a Field Programmable Gate Array (FPGA) having a microprocessor.
[0043] The method may operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers. The remote computer(s) may be a workstation, a server computer, a router, a personal computer, a portable computer, a personal digital assistant, a cellular device, a microprocessor-based entertainment appliance, a peer device or other common network node, and may include many or all of the elements described relative to the computer. The logical connections depicted include wired/wireless connectivity to a local area network (LAN) and/or larger networks, for example, a wide area network (WAN). Such LAN and WAN networking environments are commonplace in offices, and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network such as the Internet.
[0044] Embodiments of the present invention have been described in the general context of method steps which may be implemented in one embodiment by a program product including machine-executable instructions, such as program code, for example in the form of program modules executed by machines in networked environments. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Machine-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.
[0045] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
[0046] Thus, the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.

I/We Claim:

1. A gender data classification system using deep learning comprising:
a set of sensors for providing data to a deep learning model;
a database for capturing correspondences of faces by face tracking;
a data log unit operatively coupled to the one or more processing unit;
a storage unit configured to store machine-readable instruction;
a processor configured to classifying the gender data using the deep learning model and the one or more classifiers; and
a communication module configured to transmit data, wirelessly, to a central computer.
2. The gender data classification system using deep learning as claimed in claim 1, wherein the memory coupled to the one or more processors, the storage unit having stored instructions which executed by the one or more processors.
3. The gender data classification system using deep learning as claimed in claim 1, wherein the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data.
4. The gender data classification system using deep learning as claimed in claim 1, wherein the training a fast learning model based on at least the one or more modified classifiers and that portion of the data that corresponds to the one or more classification errors.
5. The gender data classification system using deep learning as claimed in claim 1, wherein the database selected from the group consisting of a Floppy Disk, a DVD, a Blu-Ray Disk, a CD, a ROM, a PROM, an EPROM, an EEPROM or a flash memory, a hard Disk, or another magnetic or optical memory, on which electronically readable control signals are stored.
6. The gender data classification system using deep learning as claimed in claim 1, wherein the communication module via a wireless transmission channel employing at least one selected from the group consisting of Wi-Fi module, cellular communication module, Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX) module, Wireless Broadband (WiBro) module, and High-Speed Downlink Packet Access (HSDPA) module.