Age Invariant Face Recognition using Convolutional Neural Network

In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier

A Software Framework for PCA-based Face Recognition

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplacable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component anaylsis (PCA) was proposed in the earliest stage; however, it is still attracting researchers in this field because of its property of reducing data dimensionality without losing important information. PCA-based face recognition has been studied for decades. There exist some image processing toolkits like OpenCV, which have implemented the PCA algorithm and associated methods. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle, which can hardly be solved by the toolkits. Furthermore, it still costs a lot of effort for software developers to integrate the implementations of the toolkits with their own applications. Therefore, the thesis provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Through various combination of these variations, at least 108 variations can be produced by the framework. Moreover, some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided

Biview face recognition in the shape–texture domain

Face recognition is one of the biometric identification methods with the highest potential. The existing face recognition algorithms relying on the texture information of face images are affected greatly by the variation of expression, scale and illumination. Whereas the algorithms based on the shape topology weaken the influence of illumination to some extent, but the impact of expression, scale and illumination on face recognition is still unsolved. To this end, we propose a new method for face recognition by integrating texture information with shape information, called biview face recognition algorithm. The texture models are constructed by using subspace learning methods and shape topologies are formed by building graphs for face images. The proposed biview face recognition method is compared with recognition algorithms merely based on texture or shape information. Experimental results of recognizing faces under the variation of illumination, expression and scale demonstrate that the performance of the proposed biview face recognition outperforms texture-based and shape-based algorithms