Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics

By Kyong I. Chang, Kevin W. Bowyer, Sudeep Sarkar, Barnabas Victor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

646 citations recorded by the source corpus.

Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/afa45f1efa72629f8a751fd16bbbda39b2e98f38

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Citations in the corpus

Face Recognition: A Literature Review (2008, background): “Recently, in [32] experiments with ear and face recognition, using the standard principal component analysis approach , showed that the recognition performance is essentially identical using ear images or face images and combining the two for multimodal recognition results in a statistically signifi”
A review of biometric technology along with trends and prospects (2014, neutral): “Table 4: Summary of behavioral modalities: Modality Feature Set Recognition Techniques Datasets Voice Spectrum, Glottal pulse features, pitch, energy, duration, rhythm, temporal features, phones, idiolects, semantics, accent, pronunciation, LPCC [204], MFCC [205], VQ [206-208], HMM [209], GMM [210, ”
Automated human identification using ear imaging (2012, background): “…mainly due to its (i) rich and stable structure that is preserved since birth and is quite unique in individuals, (ii) being invariable to the changes in pose and facial expression, and (iii) relatively immune from anxiety, privacy, and hygiene problems with several other biometric candidates.”
Human Ear Recognition in 3D (2007, methodology): “Researchers have developed several biometrics techniques using the 2D intensity images [1, chapter 13], [3], [4], [5].”
Person Identification Using Full-Body Motion and Anthropometric Biometrics from Kinect Videos (2012, background): “To date, person identification systems that incorporate one, or more [7, 8], of these biometrics tend to dominate the community.”
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition (2006, methodology): “For example, the use of 2D images of the face has the potential to provide data that might be used for iris recognition or ear recognition [ 15 ] as well.”
A Survey of Face Recognition Techniques (2009, background): “25, 26 and 27]), and reported that the infra-red imagery significantly outperformed the visible one in all the classification experiments conducted under the various above-mentioned conditions.”
Force Field Feature Extraction for Ear Biometrics (2005, methodology): “…as addressing faster implementation, invertibility, and brightness sensitivity, the technique is also validated by performing recognition on a database of ears selected from the XM2VTS face database, and by comparing the results with the more established technique of Principal Components Analysis.”
Biometric Recognition Using 3D Ear Shape (2007, methodology): “In their work, a lighting compensation technique is introduced to normalize the color appearance.”