Description:FIELD OF INVENTION
The availability of computer systems has created a variety of automated applications in personal identification. From the various characteristics of biometrics, face recognition techniques mainly face verification has become an area of active research and the application are important in law enforcement because it can be done without involving the subject. However, the influence of age progression on face verification become a challenge to determine the similarity of image pairs from individual faces considering very limited of data base availability.
BACKGROUND OF INVENTION
Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy.

SUMMARY
With the continuous improvement of science and technology, face detection and recognition are applied in more and more fields, such as the verification of identity by each application face scanning, the monitoring system of the bank self-service cash machine, the face unlocking of the mobile phone, and the new face-brushing technology. All need to pass the detection and recognition technology for the face. Under the prospect of the gradual diversification of the technology, face detection and recognition have become a technology closely related to our lives. Face detection and recognition technology not only make life easier and faster but also add a touch of technology fun. Through the face of a series of operations such as unlocking the phone, paying for the face, and intelligently identifying, using high-tech technology to ensure the security of our property and identity and to realize the combination of technology and life, it is a vital part of our lives. Sensors can be combined with many technologies to form smart sensors. Vision measurement technology has been developed into a new type of industrial testing technology, and its application scope is also expanding. Early vision measurement will be limited by the software and hardware resources of image sensors and image processing systems and is expensive, has low performance indicators, and has relatively high failure rates. The processing efficiency is not high.
DETAILED DESCRIPTION OF INVENTION
The human face provides prior perceptible information about one’s age, gender, identity, ethnicity, and mood. Alley asserts that attributes derived from human facial appearance like mood and perceived age significantly impact interpersonal behavior as is considered as essential contextual cue in social networks. Information rendered by the human face has attracted significant attention in the face image processing research community. Image-based age and age-group estimation particularly has attracted enormous research interest due to its vast application areas like age-invariant face recognition and face verification across age, among other commercial and law enforcement areas. Age estimation has been extensively studied with the aim of finding out aging patterns and variations and how to best characterize an aging face for accurate age estimation. Age estimation is a technique of automatically labeling the human face with an exact age or age group.

Figure 1: Face recognition stages
This age can be either actual age, appearance age, perceived age, or estimated age. Aging introduces significant change in facial shape in formative years and relatively large texture variations with still minor change in shape in older age groups. Shape variations in younger age groups are caused by craniofacial growth. Craniofacial studies have shown that human faces change from circular to oval as one ages. These changes lead to variations in the position of fiducial landmarks. During craniofacial development, the forehead slopes back releasing space on the cranium. The eyes, ears, mouth, and nose expand to cover interstitial space created. The chin becomes protrusive as cheeks extend. Facial skin remains moderately unchanged than shape. More literature on craniofacial development is found in. As one ages, facial blemishes like wrinkles, freckles, and age spots appear. Underneath the skin, melanin-producing cells are damaged due to exposure to the suns’ ultraviolet (UV) rays. Freckles and age spots appear due to overproduction of melanin. Consequently, light-reflecting collagen not only decreases but also becomes non-uniformly distributed making facial skin tone non-uniform. Parts adversely affected by sunlight are the upper cheek, nose, Nose Bridge, and forehead.
Aging is inevitable and uncontrollable: No one can avoid aging, advance, or delay it. The aging process is slow but irreversible.
Aging patterns are personalized: Individuals aging pattern is dependent on her/his genetic makeup as well as various extrinsic factors such as health, environmental conditions, and lifestyle.
Achieved aging patterns are temporal: Facial variations caused by aging are not permanent. Furthermore, facial variation at a particular point in time affects future appearance and does not affect previous appearance of these faces.
AGE ESTIMATION APPLICATION AREAS
Characterizing variations in facial appearance across age has many significant real-world applications. Computer-based age estimation is useful in situations where one’s age is to be determined. There are several application areas for age estimation including the following:
Age simulation
Characterization of facial appearance at different ages could be effectively used in simulating or modeling one’s age at a particular point in time. Estimated ages at different times could help in learning the aging pattern of an individual, which could assist in simulating facial appearance of the individual at some unseen age. More details on facial aging simulation could be found in. By observing aging patterns at different ages, unseen appearance could be simulated and used to find missing persons. By observing aging patterns at different ages, unseen appearance could be simulated.
Electronic customer relationship management (ECRM)
ECRM is the use of Internet-based technologies such as websites, emails, forums, and chat rooms, for effective managing of distinguished interactions with clients and individually communicating to them. Customers in different ages may have diverse preferences and expectations of a product. Therefore, companies may use automatic age estimation to monitor market trends and customize their products and services to meet needs and preferences of customers in different age groups. The problem here is how to acquire and analyze substantive personal data from all client groups without infringing on their privacy rights. With automatic age estimation, a camera can snap pictures of clients and automatically estimate their age groups in addition to collection of demographic data.
Security and surveillance
Age estimation can be used in surveillance and monitoring of alcohol and cigarette vending machines and bars for preventing underage from accessing alcoholic drinks and cigarettes and restricting children access to adult websites and movies. Age estimation can also be significant in controlling ATM money transfer fraud by monitoring a particular age group that is apt to the vice. Age estimation can also be used to improve accuracy and robustness of face recognition hence improving homeland security. Age estimation can also be used in health-care systems like robotic nurse and doctor’s expert system for customized medical services. For instance, a customized avatar can be automatically selected from a database for interacting with patients from various age groups depending on preferences.
Biometrics
Age estimation via faces is a soft biometric that can be used to compliment biometric techniques like face recognition, fingerprints, or iris in order to improve recognition, verification, or authentication accuracies. Age estimation can be applied in age-invariant face recognition, iris recognition, hand geometry recognition, and fingerprint recognition in order to improve accuracy of hard (primary) biometric system.
Employment
Some government employments like the military and police consider one’s age as a requirement. Age estimation systems could be used to determine age of the recruits during recruitment process. It is also a policy of several governments that employees should retire after reaching a particular age. Age estimation systems could also play a significant role in finding if one has reached retirement age.
Content access
With the proliferation of diverse content in televisions (TV) and the Internet, age estimation can be used to control access to unwanted content to children. A camera could be mounted on a TV to monitor people looking at it such that it switches off the TV if at a particular time unwanted content is streamed and people watching are children.
Missing persons
Age estimation role in age simulation go a step further in aiding identification of missing persons. Age simulation can be used to identify old people from their previous images for purposes of identification. Anthropometric modeling of facial aging focuses on distance measurements between facial points. Face anthropometry is the study of measuring sizes and proportions on human faces. Farkas defined face anthropometry based on measurements taken from 57 landmark points on human faces. Landmark points are identified by abbreviation of their respective anatomical names. For instance, the eye inner corner is en for endocanthion while front of the ear is t for tragion.

Figure 2: shows some of the points used to describe a face

Figure 3: shows sample measurements of these distances.
Facial measurements could be taken at different ages for instance from childhood to old age. Ratios of distances between facial landmarks like the eyes, nose, mouth, ear, chin, and forehead are measured across age. Facial measurements are used to determine the aging pattern of an individual at a particular age and hence used to discriminate between ages and age groups. This approach embraces studies in craniofacial development theory. Craniofacial development theory uses cardioid strain transformation mathematical model to describe a person’s facial growth from infancy to adult age. This model defines a circle to track facial growth by tracking variations in radius of the circle as
Where R is the initial radius of the circle, θ is the initial angle formed with the vertical axis, k is a parameter that increases with time, and R′ is the successive growth of the circle over time.
DETAILED DESCRIPTION OF DIAGRAM
Figure 1: Face recognition stages
Figure 2: shows some of the points used to describe a face
Figure 3: shows sample measurements of these distances. , Claims:1. Image processing and face detection analysis on face verification and the performance based on the age stages claims that there has been enormous effort from both academia and industry dedicated towards modelling age estimation, designing of algorithms, aging face dataset collection, and protocols for evaluating system performance.
2. On an apparatus including a processor configured for image processing, automatically identifying a group of pixels that correspond to an image of a face within the digital image.
3. Generating in-camera, capturing or otherwise obtaining in-camera a collection of low resolution images including said face.
4. Tracking said face within said collection of low resolution images.
5. Determining default values of one or more parameters of at least some portion of said digital image and adjusting values of the one or more parameters within the digitally-detected image based upon an analysis of said digital image including said image of said face and said default values.
6. Wherein the one or more parameters include an orientation of said face. The method of claim 1, the one or more parameters comprise of a mask that defines one or more regions where the one or more parameters are valid.
7. The method of claim 2, the mask further comprising a continuous presentation of varying strength within different sub-regions of said one or more regions.

8. The method of claim 2, said one or more parameters comprising identical parameters that differ in value based on said mask.