Enhancement Ear-based Biometric System Using a Modified AdaBoost Method





AdaBoost, Classifier, Ear, KNN, RMSE, SIFT, SVM


          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed system's performance. method, the classification accuracy has been compared using different types of classifiers. These classifiers are Naïve Bayesian, KNN, J48, and SVM. The range of the identification accuracy for all the processed databases using the proposed scenario is between (%93.8- %97.8). The system was executed using MATHLAB R2017, 2.10 GHz processor, and 4 GB RAM.


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Omran M, AlShemmary E, Towards Accurate Pupil Detection Based on Morphology and Hough Transform. Baghdad Sci.J., 2020; 17(2): 583-590.

Ahmed H.M, Hameed S.R, Eye Detection using Helmholtz Principle. Baghdad Sci. J., 2019; 16 (4): 1087-1092.

Hourali F, Gharravi S, An Ear Recognition Method Based on Rotation Invariant Transformed DCT. Int J Electr Comput., 2017; 7(5): 2895-2901.

Ahmed M. Alkababji, Omar H. Mohammed, Real-time ear recognition using deep learning. TELKOMNIKA. 2021; 19(2): 523-530.

Arulananth T. S., Baskar, Human face detection, and recognition using contour generation and matching algorithm. IJEECS. 2019; 16(2):709-714.

Horkaew P, Khaminkure A, Suesat N, Puttinaovarat S, Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model. Baghdad Sci.J. 2020; 17(1):190-198.

Sharma A, Lalwani N, Edinburgh M.R.M., Biometric Identification using Human. Int J Eng Adv Tech. 2019; 9(1): 4893-4898.

Chengsheng TU, Huacheng LIU, Bing XU., AdaBoost typical Algorithm, and its application research. Matec Web Conf. 2017;139,00222:1-6. Available from: http://doi.org/.1051/matecconf/ 201713900222.

Flaih H N, Abdulrazzaq H I, Pose Invariant Palm Vein Identification System using Convolutional Neural Network. Baghdad Sci. J. 2018; 15(4): 502-509.

Hussein M M, Mutlag A H, Shareef H, Developed artificial neural network-based human face recognition. Indones J Electr Eng Comput. Sci. 2019;16(3):1279-1285.http://doi.org/10.11591/ijeecs.v16.i3.pp1279-1285

Wang Fei, Zhongheng Liw, Fang He, Rong Wang, Weizhong Yu, Feiping Nie, Feature Learning Viewpoint of AdaBoost and a New Algorithm. IEEE Access. 2019; 1-9. Available from: http://doi.org/ 10.1109/ACCESS.2019.2947359, IEEE Access.

Lorentzon M, Feature extraction for image selection using machine learning. 2017. Thesis, Computer Vision Laboratory Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden.

Zhou W, GAO S, Zhang L, Lou X, Histogram of Oriented Gradients Feature Extraction from Raw Bayer Pattern Images. IEEE Trans Circu Syst II; 2020:1-6. Available from: http://doi.org/ 10.1109/TCSII.2020.2980557.

Ekhlas K G, Suha M S, Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor. Baghdad Sci.J. 2017; 14 (3): 651-661.

Karami E, Shehat M, Simth A, Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations. Elect Comput Eng Conf. Faculty of Engineering and Applied Sciences; 2017; 1-5.

Kohlakala A, Thesis: Ear-based biometric authentication. Stellenbosch University; 2019. Available from: http://scholar.sun.ac.za/handle/10019.1/105976.

Earnest E H, Identification of Individuals from Ears in Real-World Conditions, A Ph.D. dissertation submitted to University of South Florida, Department of Computer Science and Engineering -College of Engineering, 2018.