Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms

Abstract

This paper proposes an improved performance algorithm of face recognition to identify two face mismatch pairs in cases of incorrect decisions. The primary feature of this method is to deploy the similarity score with respect to Gaussian components between two previously unseen faces. Unlike the conventional classical vector distance measurement, our algorithms also consider the plot of summation of the similarity index versus face feature vector distance. A mixture of Gaussian models of labeled faces is also widely applicable to different biometric system parameters. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to that of the conventional algorithm by an average accuracy of up to 1.15% and 16.87% when compared with 3x3 Multi-Region Histogram (MRH) direct-bag-of-features and Principal Component Analysis (PCA)-based face recognition systems, respectively. The experimental results show that similarity score consideration is more discriminative for face recognition compared to feature distance. Experimental results of Labeled Face in the Wild (LFW) data set demonstrate that our algorithms are suitable for real applications probe-to-gallery identification of face recognition systems. Moreover, this proposed method can also be applied to other recognition systems and therefore additionally improves recognition scores.

Keywords

• gaussian mixture model
• expectation-maximization algorithm
• similarity score
• probabilistic histogram
• face recognition