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A Study on Human Recognition Using Auricle and Side View Face Images

Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

Face profile, the side view of the face, provides biometric discriminative information complimentary to the information provided by frontal view face images. Biometric systems that deal with non-cooperative individuals in unconstrained environments, such as those encountered in surveillance applications, can benefit from profile face images. Part of a profile face image is the human ear, which is referred to as the auricle. Human ears have discriminative information across individuals and thus, are useful for human recognition. In the current literature, there is no clear definition for what a face profile is. In this study, we discuss challenges related to this problem from recognition performance aspect to identify which parts of the head side view provide distinctive identity cues. We perform an evaluation study assessing the recognition performance of the distinct parts of the head side view using four databases (FERET, WVU, UND, and USTB). The contributions of this paper are three-fold: (i) by investigating which parts of the head side view increase the probability of successful human authentication, we determined that ears provide main features in the head side view. The rank-1 identification performance using the ear alone is about 90%. (ii) we examined various feature extraction methods to learn the best features for head side view and auricle recognition including shape-based, namely Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF); and texture-based, namely Multi scale Local Binary Patterns (MLBP), Local Ternary Patterns (LTP). We determined that texture-based techniques perform better considering that the MLBP yielded the best performance with 90.20% rank-1 identification; and (iii) we evaluated the effect of different fusion schemes, at the image, feature, and score levels, on the recognition performance. Weighted Score fusion of face profile and ear has the best score with 91.14% rank-1 identification.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.West Virginia UniversityMorgantownUSA
  2. 2.Cairo UniversityCairoEgypt

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