Supervised and Unsupervised Data Engineering for Multimedia Data
by Suman Kumar Swarnkar, J. P. Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore, Tien Anh Tran
12Supervised and Unsupervised Learning Techniques for Biometric Systems
Pallavi Pandey*, Yogita Yashveer Raghav, Sarita Gulia, Sagar Aggarwal and Nitin Kumar
Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, India
Abstract
In the domain of biometric systems, where individual identities are confirmed through distinctive traits like fingerprints, facial geometry, and gait patterns, the application of supervised and unsupervised machine learning techniques plays a pivotal role. This chapter provides a comprehensive examination of these approaches within the context of biometric systems. Supervised learning, which relies on labeled data to train models for predicting outcomes, has proven effective in various biometric applications, employing algorithms such as Convolutional Neural Networks, Support Vector Machines, logistic regression, and Decision trees. Unsupervised learning, in contrast, excels in automatic feature extraction, data analysis, and learning strategy creation. While it may not be the primary choice for identification, it contributes significantly to improved feature fusion and data analysis. This chapter offers a detailed exploration of these machine learning techniques, assessing their suitability for both identification and verification processes. Furthermore, it addresses the persistent challenges faced in biometric system development, ranging from handling numerous identities and security ...