Article ID Journal Published Year Pages File Type
528628 Image and Vision Computing 2013 9 Pages PDF
Abstract

•Collected thermal profile face database (using a middle-wave infrared “3-5 microns” camera).•Developed a fully automated thermal ear recognition system for real-time human identification (works in day or night).•Local Ternary Pattern yields (Rank-1 = 80.68% and 68.18%) using manually and automatically segmented ears respectively.•Score-Level fusion of the Local Ternary Pattern (LTP) and Local Binary Pattern (LBP) enhanced the performance by ~ 5%.

In this paper the problem of human ear recognition in the Mid-wave infrared (MWIR) spectrum is studied in order to illustrate the advantages and limitations of the ear-based biometrics that can operate in day and night time environments. The main contributions of this work are two-fold: First, a dual-band database is assembled that consists of visible (baseline) and mid-wave IR left and right profile face images. Profile face images were collected using a high definition mid-wave IR camera that is capable of acquiring thermal imprints of human skin. Second, a fully automated, thermal imaging based, ear recognition system is proposed that is designed and developed to perform real-time human identification.The proposed system tests several feature extraction methods, namely: (i) intensity-based such as independent component analysis (ICA), principal component analysis (PCA), and linear discriminant analysis (LDA); (ii) shape-based such as scale invariant feature transform (SIFT); as well as (iii) texture-based such as local binary patterns (LBP), and local ternary patterns (LTP). Experimental results suggest that LTP (followed by LBP) yields the best performance (Rank1 = 80:68%) on manually segmented ears and (Rank1 = 68:18%) on ear images that are automatically detected and segmented. By fusing the matching scores obtained by LBP and LTP, the identification performance increases by about 5%. Although these results are promising, the outcomes of our study suggest that the design and development of automated ear-based recognition systems that can operate efficiently in the lower part of the passive IR spectrum are very challenging tasks.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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