Abstract

Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.

Highlights

  • As the progress of the computer vision and machine learning, person identification and verification for security considerations become practical and play an important role for modern smart living applications

  • If we divide the face region into several subfaces, called modules, any face recognition algorithm will become a new module face recognition algorithm, which can avoid the serious degradation of recognition performance to solve the occlusion problem

  • We proposed the texture histogram difference of the module to detect the its occlusion tendency of the input face image

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Summary

Introduction

As the progress of the computer vision and machine learning, person identification and verification for security considerations become practical and play an important role for modern smart living applications. There are numerous algorithms [1–4] made important contributions to face recognition These approaches can be divided into two categories, holistic based and modular based methods [5]. Naseem et al [8] proposed linear regression classification (LRC) and its modular design to solve occlusion problems. The robust face recognition algorithms based on various features, such as Huber loss [25], local binary feature [26] and topology preserving structure matching (TPSM) [27] are proposed to solve occlusion problems successfully. We propose a better occlusion tendency detection by using texture histogram (TH) to distinguish occluded and non-occluded modules. The realization of the partially-occluded face recognition system based on the WMLRC-TH method in mobile platforms are addressed.

Module linear regression classification for face recognition
Linear regression classification
Module linear regression classification (MLRC)
Weighted MLRC by detection of occluded modules
Occlusion detection by texture histogram
Weighted MLRC method by texture histogram
Experimental results
Experiments on AR database
Methods
Android based system implementation
Conclusion
Full Text
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