On ear-based human identification in the mid-wave infrared spectrum

• 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.