Age Estimation using OLPP Features

Aging face recognition poses as a key difficulty in facial recognition. It refers to identification of a person face over varied ages. It includes issues like age estimation, progression and verification. Non-availability of facial aging databases make it harder for any system to achieve good accuracy as there are no good training sets available. Age estimation when done correctly has a varied number of real life applications like age detailed vending machines, age specific access control and finding missing children. This paper implements age estimation using Park Aging Mind laboratory - Face database that contains meta data and 293 unique images of 293 individuals. Ages range from 19 to 45 with a median age of 32. Race is classified into two categories : African-American and Caucasian giving an accuracy of 98%. Sobel edge detection and Orthogonal locality preservation projection were used as the dominant features for the training and testing of age estimation. A Multi-stage binary classification using support vector machine was used to classify images into an age group thereafter predicting an individual’s age. The effectiveness of this method can be increased by using a large data set with a wider age range.


INTRODUCTION
Security is a property which measures the degree of resisting some harmful attack on any vulnerable entity.The entity can represent anything that exists in the nature.It can represent some human being, object, company, country, etc.All these are associated with security in order to achieve their goals and objectives.Any entity can be vulnerable to any kind of attack or harm.It is the security of that system which makes it strong enough to defend against those harmful attacks and tackle the situation accordingly.Every system essentially needs some security mechanism.The main reason for the systems to need security is the rapid development in the field of information processing.Data and information are continuously increasing day by day.This makes it essential to provide some security mechanisms in order to protect the data.It is unacceptable when situations occur where information is lost or modified.Some entities which perform in real-time mode are totally dependent on the integrity of the information that is being processed.Normally data security is compromised during the process of transmission.Any attacker can meddle with the information that is being transmitted across a certain medium.Attacks can occur even when the information is not being transmitted.In order to avoid all these serious circumstances, we essentially need security to any system that we define.
There are many ways wherein we can provide security.Some of them can be listed as: One of the most useful ways to provide security is to use the facial recognition technique.In this technique, a picture of a person is taken and some pre-processing is done.It will then be checked if that particular picture is present in the database or not.If it is not, then it will be added to it and some parameters will be set accordingly.The picture present in the database could be an old picture of the same person.But we cannot refuse authorizing him just because the face looks similar and the same.The currently obtained facial will be processed to extract some values which will be compared to the values of the old picture and based on this result the person will either be authenticated or some suitable message will be displayed on error.
Biometric security involves the actual human interaction with a system to authenticate the user.This proves to be an efficient system as it uses the characteristics of human body and parts to provide security.Every human body is unique and can never be the same as other.There are many ways through which we can achieve this.Most common among them are Fingerprint scanner and facial recognition system.Every individual's fingerprint is not same and can never be the same for anyone.The person will be authenticated if his fingerprint matches with the fingerprint present in his database entry.A more efficient way of achieving biometric security is to use the facial recognition system.This is more preferred as it has no direct contact of the human body with the machine or the system.This doesn't require any direct interaction with the person.This can also be used in the crime detection purposes.Once a person's face is recorded in the database, there is nothing that can prove it wrong when it is being cross-checked.This can be implemented as a functionally independent component of a system.Facial recognition has several uses in daily actions, it allows identity verification of individuals in banking transactions, online payments, control access to high security facilities etc. However lately facial recognition is widely used to uniquely authenticate an individual as this technology doesn't require a contact between the sensor and the individual, which enables remote authentication and authorisation.Some of the advantages of facial recognition are that it can be operated without the cooperation of actual human beings.These devices when properly installed can be used in companies, malls, airports and other similar vulnerable areas.This can also be used in public places without people getting to know about it.This is among the key methods used in mass identification.
Face recognition comes with its own set of challenges like variation of Pose, Illumination, Occlusion, low resolution and aging.The drawbacks in this system which does need some special attention.It might not work in some unusual and strange circumstances.The simple example being, recognizing a face from a different angle.This will pose a problem if we try to compare two faces where one of them is deviated in an angle which is more than the one that can be used.One more disadvantage is that the system becomes less effective if a complex facial recognition system is used.Facial expressions should be similar while comparing.All these make it a not-so-perfect technique to be used while providing security.
Aging is an unescapable process.Aging causes major differences in the outlook of human faces over a period of time.Some features of aging are controllable whilst others are not.Dimensionality reductions are a form of problems faced during information processing.Some linear projective maps such as Locality Preserving Projections (LPP) should be seen as an alternative to the Principal Component Analysis (PCA), where the neighbourhood data set is optimally preserved after solving a variation problem.In an ambient space, the LPP can be obtained by figuring out the optimal linear approximations to the Eigen functions of the Laplace Beltrami operator on the manifold, which is formed when high dimensional data lies on the low dimensional data.The linear approximation of the nonlinear Laplacian Eigen map is the concept of LPP.In 'Locality Preserving Projections' algorithm ( Xiaofei He, 2014), the adjacency graph will be constructed at the first, then weights are chosen and Eigen maps are generated.These maps are simple, linear and defined everywhere.It is capable of discovering the non-linear structure of data manifold even though this is a linear algorithm.But LPP has a limitation where it cannot solve the recognition problem.To overcome this problem, 'Face recognition using discriminant Locality Preserving Projections' paper (W.yu et al, 2006) proposes a new method called Discriminant Locality Preserving Projections (DLPP) which benefits in two aspects where subspace is found to discriminate various facial classes and also reducing the noise.DLPP is better than LPP in terms of recognition accuracy.But in some other cases it is as efficient as LPP.

MATERIALS Data Collection
Availability of facial databases forms the biggest obstacle in facial recognition.There are several databases for the study of the pose, illumination, resolution and expression.Procuring these databases is easy as they were created by taking photos of subjects under controlled conditions by changing one variable at a time.Unfortunately the same cannot be performed for facial ageing database as we would have to gather the same subject's photos over a number of years.
Apart from the images available for other purposes there are a number of images available on the internet.Nonetheless it is highly unreliable as it is difficult to find the age of the subject in the image.Even the application of makeup and plastic surgery poses as a problem as it masks the facial characteristics that help determine facial age.
The age estimation algorithm was developed and tested using the non-commercial version of the Park Aging Mind laboratory -Face database (Minear, M. & Park, D.C. ,2004).This was a controlled collection of images (i.e., the images were not collected in real-world conditions containing noise and various forms of image distortion or obstruction).The dataset contained metadata in the form of age, gender, race, color, expression in .bmpfile format.The collection included the total of 290 unique images of 290 individuals.Ages range from 19 to 45 with a median age of 32.II show the distribution of images used in this study by gender and ancestry and by age respectively.It can be observed that the gender distribution was balanced.Similarly there was an uneven ancestry distribution with Caucasian origin represented by clear majority of pictures.Looking at age distribution the range 19-45 was quite unevenly distributed with a clear majority of the age range 18-29, however the ranges <19 and 45+ were unavailable.

Pre-Processing
The raw facial images were pre-processed.The pre-processing stage included face and eye position detection, angle normalization, image resizing.These procedures were implemented using the Viola-Jones algorithm cascade object detectors for facial and facial feature recognition.To aid the edge detection algorithm, histogram equalisation and image sharpening was applied on the facial image to enhance the edges in the image.The final step of image pre-processing included the detection of person's ethnicity.The ethnicity detection algorithm iteratively scanned all facial regions using a small spatial window and calculated average values of the pixel color intensities within each window.The race was then determined by comparing these values with an arbitrary threshold determined experimentally.

Feature Extraction Sobel Features
The sobel filter, sometimes called as the sobel operator is used in computer vision and image processing for mainly edge detection to create highlighting edges.It is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function.The result of the Sobel-Feldman operator is either the corresponding gradient vector or the norm of this vector.It is inexpensive in terms of computations as it is based on convolution of the image with a small, separable, and integer-valued filter in the horizontal and vertical direction.
The sobel operator uses two 3×3 kernels -one for horizontal changes, and one for vertical which are convolved with the original image to calculate approximations of the derivatives.If we define A as the source image, and Gx and Gy are two images which at each point contain the horizontal and vertical derivative approximations respectively, the computations are as follows 17 : Where here denotes the 2-dimensional signal processing convolution operation.a differentiation kernel, they compute the gradient with smoothing.For example, can be written as The x-coordinate is defined here as increasing in the "right"-direction, and the y-coordinate is defined as increasing in the "down"direction.At each point in the image, the resulting gradient approximations can be combined to give the gradient magnitude, using: G = Using this information, we can also calculate the gradient's direction: Where, for example, Θ is 0 for a vertical edge which is lighter on the right side.

OLPP Features
The Sobel features are subjected to orthogonal locality preservation projection (OLPP) algorithm.Locality Preserving Projections (LPP) are linear projective maps that arise by solving a vibrational problem that optimally preserves the neighbourhood structure of the data set.LPP should be seen as an alternative to Principal Component Analysis (PCA) -a classical linear technique that projects the data along the directions of maximal variance.LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigen maps or Locally Linear Embedding.yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points.LPP may be conducted in the original space or in the reproducing kernel Hilbert space into which data points are mapped.In simple words LPP keeps local structure of data intact.I.e.they keep data points near to a data point as nearly as possible.The LPP algorithm is as follows: Locality Preserving Projection (LPP) is a linear approximation of the nonlinear Laplacian Eigen map (A.k.Jain et al, 2012) The OLPP has the orthogonally property.It builds an adjacency map which best represents the geometry of the low dimension facial manifold and class attributes between the sample points.In this way, the OLPP preserves the locality characteristics of images compared to that of LPP which is nonorthogonal, which prohibits data reconstruct after the dimension reduction is performed.

Multistage Binary Age Estimation (Msae) Classifiers
In machine learning, there are a number of classifiers that analyse data for classification and regression.Classifiers like Support Vector Machine  Each stage of the classification procedure decision aiming to determine if the test image of only two non-overlapping age groups.At each stage of the multilayer classification procedure the system uses an independent binary classifier: a single layer feed trained on OLPP features and a support vector machine (SVM).This determines how to narrow down the age groups at the next step based on the probability with which the classification was made and a pre-defined decision table look-up process.The tested age range groups for each stage of classification were also pre-defined.

Fig. 5 . 1 :
Fig. 5.1: Algorithm for sobel edge detection method(O.R. Vincent et al, 2009) (SVM), k-Nearest Neighbour (kNN), and Naïve Bayes etc. are widely used depending on the testing and training dataset.SVM is one such classifier that uses a set of training examples and marks them as belonging to either one of two categories.It is a non-probabilistic binary linear classifier as it builds a model that assigns new examples to either of two categories.SVM builds a model that represents examples as points in space such that separate categories are divided noticeably by a wide gap.Prediction or classification is done on the basis of

Fig. 5 . 3 :
Fig. 5.3: Algorithm for Locality Preservation Projection ( Xiaofei He et al, 2014) Facial aging effects vary in the childhood age to that of the adult age.Aging effects include bone movement, bone growth, skin related changes like wrinkles and reduction of muscle strength.Usually craniological growth of the facial bones takes place during childhood which determines the shape and the structure of the face whereas during adult ages textural changes like skin deformations occur.However, researchers who carried out work in studying the process of age estimation by humans (Rhodes 2009) conclude that humans are not so accurate in age estimation hence the possibility of developing automatic facial age estimation methods were proposed.In automatic facial age estimation the goal is to use algorithms to determine a person's age.It includes face detection, feature extraction, feature vector formulation and classification.The output of the classification can either be an estimated age, age group or even a binary result indicating if the age belongs within an age range.Age classification is the preferred output as it only a rough estimate of a person's age is required.Thus range considered also becomes an important factor as facial features vary over different age groups.The commonly used error metric for age estimation problems are Mean Absolute Error (MAE) and Cumulative Score (CS).MAE provides the actual and the estimated faces in a test set whereas CS that offers a percentage where the estimation error is less than a threshold.Both methods can be used to for performance evaluation of the system.Face identification and verification (Richa Singh et al, 2007) is implemented by suggesting an algorithm which lists the gallery and probes face image in Polar Coordinate domain.It lessens the facial feature changes which occur due to aging.It takes the face image of the current and former and generates a new image based on the phase congruency.It uses local feature extraction and resolution to determine the assorted form of the face image.It attains an enhancement in Facial recognition and authentication by about 25%.HFA) on age variant features.This method separates the age-specific facial signs that change gradually over time.It then operates on age, by changing it to a target age group using sparse reconstruction, to add aging effects on the face texture and then enhances it by facial shape transformation.The aging and rejuvenating results attained on MORPH, FG-NET, IRIP show that the generated aging rendering are consistent and substantial.Whereas in another method (J.Qiu et al ,2016) presents a new aggregation based deep network to extract aging features from facial images on age variant features to achieve age detection and prediction.They employ a regionspecific convolutional neural network (CNN) at lower layers that are hierarchically aggregated into consecutive higher layers.Experimental results of age prediction on the MORPH-II databases prove that this outperforms other state-of-the-art systems with a mean error of 3.41 for age detection and is relatively reliable and robust across race at 4.72 and gender at 4.43.
Previous WorkFa c e r e c o g n i t i o n c o n t a i n s fa c e identification, verification and matching.