Estimation of Age Group using Histogram of Oriented gradients and Neural Network

Requirement of Project: Face images are being increasingly used as additional means of authentication in applications of high security zone. But with age progression the facial features changes and the database needs to be updated regularly which is a tedious task. So we need to address the issue of facial aging and come up with a mechanism that identifies a person in spite of aging. In my project, effective age group estimation using face features like texture and shape from human face image are proposed[2]. For better performance, the geometric features of facial image like wrinkle geography, face angle, left to right eye distance, eye to nose distance, eye to chin distance and eye to lip distance are calculated. Based on the texture and shape information, age classification is done using KNN & SVM algorithm (Best algorithm according to many research paper during my research)." Proposed System: "In this report, few classification and feature extraction techniques used for age group classification. In this report first we attempt to combining two type of face features using haar features extraction (Wrinkle features and Geometrical Features) also used viola Jones for face detection. Age estimation based on the graphical model structure is proposed. Three popular features, PCA (Principal Component analysis), HOG and Haarfeatures, are exploited in our work, and three different graphical model structures considering spatial information and hidden topics are proposed and implemented. The experimental results showed that our model performs classification techniques like SVM (support vector machine), KNN and Neural network and the comparisons between features extraction algorithm and classification techniques in order to obtain best output. features are also presented and discussed. Until now, the model we proposed hasn’t been well-tuned, and we’ll try to improve it for the future works." Research in those areas has been conducted for more than 30 years. "Traditionally, face recognition uses for identification of documents such as land registration, passports, driver’s licenses, and recognition of a human in a security area. [22] --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 30-10-2017 Date of acceptance: 09-11-2017 --------------------------------------------------------------------------------------------------------------------------------------I. GENERAL INTRODUCTION Most of the facial variants such as identity, expression, emotions and gender have been widely studied, in the case of research of recognition. Automatic age estimation is one such area that has been rarely explored by the researchers. With the evolution of a human, the features of the face keep on changing with age. This project is providing a new combine approach of feature selection for age group classification algorithms. Further this process is further classified into three main stages: first one is Pre-processing, second is Feature Extraction (Haar feature extraction), and the last stage is classification. For feature extraction phase we used two techniques 1) Wrinkle features and 2) Geometrical features for the face pattern recognition [11]. We know that Wrinkle features are well enough to differentiate between the adult and senior, Geometrical features is good to create difference between child and adult/senior. That is why we used a combine technique of wrinkle and geometrical so that they can solve each other problems and provide the best output. These two approaches are defined below: 1.1.1) Geometrical features This include face angle, left eye to right eye distance, eyeball, eye to nose distance, eye to chin distance and eye to lip distance that is further calculated by making use of the best feature selection algorithm 1.1.2) Wrinkle features Age classification based on the texture and shape information is done by using suggested hybrid algorithm which includes Fuzzy logic and Neural Network. Depending on a number of groups age ranges are then classified dynamically using hybridization algorithms individually. In our research, most facial features like identity, expression, emotions and gender has been majorly focused. Automatic age estimation is one area that has been rarely explored till date. As age increases, the Estimation of Age Group using Histogram of Oriented gradients and Neural Network 44 feature of the face keeps on changing. This project provides a comparison study of classification techniques (SVM, KNN algorithm) and these falls under the category of the best classification algorithms. Entire process is divided into three stages: Pre-processing, Feature Extraction (Haar feature extraction) [75], classification (above mentioned algorithm). Machine learning phase uses different classification algorithm approach in order to provide the best solution for pattern recognition. That is why we are using one hybrid technique of wrinkle and geometrical by which they can solve each other problems and provide the best results. Here we make use of two important features of the face which are responsible for age identification. Personal identification and verification has evolved as an active area of research these days. As biometric characteristics of the individual are unique person to person, biometric authentication techniques have a great advantage over traditional authentication techniques. Recognition of face is one of the widely used biometric methods which are used to identify individuals by their face features. Face, voice, fingerprint, iris, ear, retina are the most commonly used for authentication purpose. Research in those areas has been conducted for more than 30 years. Face recognition is beneficial for identification of documents such as for land registration, passports, driver’s licenses, and recognition of a human in a security area [84]. Face images are highly used as additional means of authentication in applications having high security zone. But with increase in age the facial features also keep on changes and the database needs to be updated regularly which is one very tedious task. Hence we need to address this issue of facial aging and come up with a solution that identifies a person without any age limits. In this thesis, effective age group estimation using face features like texture and shape from human face image is proposed. For getting efficient results, the geometric features of the facial image like wrinkle geography, face angle, left to right eye distance, eye to nose distance, eye to chin distance and eye to lip distance are calculated [90]. Based on the texture and shape information, age classification is done by making use of classification