Ear Recognition Based on Gabor Features and KFDA

Table 1

Representative feature extraction methods and their performance evaluation.

Reference	Description	Dataset	Performance
Structural feature extraction methods
[13]	Burge and Burger (1997) use the main curve segments to form Voronoi diagram and use adjacency graph matching based algorithm for authentication. But the curve segments will be affected by changes in camera-to-ear orientation or lighting variation.	—	—
[14]	Moreno et al. (1999) used feature points of outer ear contour and information obtained from ear shape and wrinkles for ear recognition. The compression network is applied for classification.	28 subjects, 168 images, 6 images for each subject	Rank-1: 93%
[15]	Mu et al. (2004) proposed a long axis based shape and structural feature extraction method; the shape feature consisted of the curve fitting parameters of the outer ear contour, the structural feature was composed of ratios of the length of key sections to the length of the long axis, and nearest neighborhood classifier was used for recognition.	USTB dataset2: 77 subjects, 4 images for each subject	Rank-1: 85%
[16]	Choras (2005) proposed a geometrical feature extraction method based on number of pixels that have the same radius in a circle with the centre in the centroid and on the main curves.	240 images	—
Local feature extraction methods
[17]	Hurley et al. (2005) proposed the force field transformation method. The ear images are treated as array of mutually attracting particles that act as the source of Gaussian force field. The force field transforms of the ear images were taken and the force fields were then converted to convergence fields. Then Fourier based cross-correlation techniques were used to perform multiplicative template matching on ternary thresholded convergence maps.	XM2VTS face profile subset (252 subjects)	Rank-1: 99.2%
[18]	Nanni and Lumini (2007) proposed a local approach. A multimatcher system was proposed where each matcher was trained using features extracted from the convolution of each subwindow with a bank of Gabor filters. The best matchers, corresponding to the most discriminative subwindows, were selected by Sequential Forward Floating Selection where the fitness function was related to the optimization of the ear recognition performance. Ear recognition was made using sum rule based decision level fusion.	UND collection E (114 subjects)	Rank-1: 80%; EER: 4.3%
[19]	Bustard and Nixon (2010) proposed an ear registration and recognition method by treating the ear as a planar surface and creating a homography transform using SIFT feature matches. Ear recognition under partial occlusion was discussed in this paper. The relationship between occlusion percentage and recognition rate was presented.	XM2VTS face profile dataset (63 subjects)	Rank-1: 92% (30% occlusion from above),; 92% (30% occlusion from left side)
[20]	Arbab-Zavar and Nixon (2011) proposed a model-based approach for ear recognition. The model was a partwise description of the ear derived by a stochastic clustering on a set of scale invariant features of the training set. The outer ear curves were further analyzed with log-Gabor filter. Ear recognition was made by fusing the model-based and outer ear metrics.	XM2VTS face profile dataset (63 subjects)	Rank-1: 89.4% (30% occlusion from above)
Holistic feature extraction methods
[21]	Chang et al. (2003) used standard PCA to compare face and ear and concluded that ear and face did not have much difference on recognition performance.	Human ID Database (197 subjects)	Rank-1: 70.5% for face, 71.6% for ear
[22]	Yuan et al. (2006) proposed an improved Nonnegative Matrix Factorization with Sparseness Constraint for ear recognition with occlusion. The ear image was divided into three parts with no overlapping. INMFSC was applied for feature extraction. The final classification was based on a Gaussian model based classifier.	USTB dataset3 (79 subjects)	Rank 1: ~91% (for 10% occlusion from above)
[23]	Dun and Mu (2009) proposed an ICA based ear recognition method through nonlinear adaptive feature fusion. Firstly, two types of complimentary features are extracted using ICA. These features are then concatenated with different weight to form a high-dimensional fused feature. Then the feature dimension was reduced by Kernel PCA. The final decision was made by nearest neighbor classifier.	USTB dataset3 (79 subjects), and USTB dataset4 (150 subjects)	Rank-1: ≥90% (for pose variation within 15°)
[24]	Wang et al. (2008) proposed ear recognition based on Local Binary Pattern. Ear images were decomposed by Haar wavelet transform. Then Uniform LBP, combined with block-based and multiresolution methods, was applied to describe the texture features. Finally, the texture features are classified by the nearest neighbor method.	USTB dataset3 (79 subjects)	Rank-1: ≥92% (for pose variation within 20°)
[25]	Zhou et al. (2010) proposed ear recognition via sparse representation. Gabor features are used to develop a dictionary. Classification is performed by extracting features from the test data and using the dictionary for representing the test data. The class of the test data is then determined based upon the involvement of the dictionary entries in its representation.	UND G subset, 39 subjects	Rank-1: 98.46% (4 images for training and 2 images for testing)