Hybrid ear recognition framework based on passive human identification - Alemran, Ahmed Ali (2022)
Current identification of passive detection has been attention in the modern world due to the system's robustness as an ear recognition framework based on a multiclassifier and attempt to create user patterns via extracted features from ear images, which have unique individual identities. The collected features from the ear intersection points and the angles bounded between curves using different descriptors and classifiers are considered unique information used to generate unique features. The proposed framework commenced with the extraction of eight sets of features (LBP, BSIF, LPQ, RILPQ, POEM, HOG, DSIFT, and Gabor) from 2D ear images. Subsequently, ELM and SVM classifiers were trained on each set of features. Seven combination rules (MR, AR, GWAR, ICWAR, Borda, DS, and AV (GWAR, Borda, DS)) were utilized to acquire a total of 16 classifiers. Also, two optimization rules; genetic algorithm and brute force were proposed for accuracy enhancement. The AWE and the USTB datasets were utilized in the development, evaluation, and validation of an ear recognition framework dataset. So, some vulnerabilities are observed in datasets and all challenges for ear biometrics. The research findings showed that combining classifiers using different sets of features yields better performance compared to using individual classifiers. However, using one classifier or limited number is not enough to solve the problem of ear recognition with different challenges such as Pose, Occlusion, Illumination, Blurry image, Rotation, Lighting, Scale, and Translation. The validation of such a framework using the AWE dataset showed that the SVM and ELM in combination with modern descriptors managed to enhance the recognition. Rank-1 accuracy also reached 99% with Genetic Algorithm optimization, and 98% with brute-force AR and brute-force GWAR. These results are compared to other results in the literature and found to be superior. In conclusion, the main findings showed that the proposed framework consisting of two classifiers SVM and ELM trained with selected features and the combination rules managed to attain higher accuracy in-ear recognition compared with previous studies. This ear recognition framework is a major step towards the recognition of individuals from ears in real-world conditions. This study implies that the proposed ear recognition framework based on ELM and SVM classifiers with combination and optimization rules can be utilized to improve the effectiveness of passive human recognition where security is of utmost importance.