Back close

Performance Analysis of NASNet on Unconstrained Ear Recognition

Publication Type : Book Chapter

Publisher : Springer International Publishing

Source : Nature Inspired Computing for Data Science, Springer International Publishing, p.57-82, Springer, Cham (2020)

Url :

ISBN : 9783030338206

Keywords : Convolutional neural network, Deep learning, NASNet, Unconstrained ear recognition

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN), Computer Science, Electronics and Communication

Year : 2020

Abstract : Recent times are witnessing greater influence of Artificial Intelligence (AI) on identification of subjects based on biometrics. Traditional biometric recognition algorithms, which were constrained by their data acquisition methods, are now giving way to data collected in the unconstrained manner. Practically, the data can be exposed to factors like varying environmental conditions, image quality, pose, image clutter and background changes. Our research is focused on the biometric recognition, through identification of the subject from the ear. The images for the same are collected in an unconstrained manner. The advancements in deep neural network can be sighted as the main reason for such a quantum leap. The primary challenge of the present work is the selection of appropriate deep learning architecture for unconstrained ear recognition. Therefore the performance analysis of various pretrained networks such as VGGNet, Inception Net, ResNet, Mobile Net and NASNet is attempted here. The third challenge we addressed is to optimize the computational resources by reducing the number of learnable parameters while reducing the number of operations. Optimization of selected cells as in NASNet architecture is a paradigm shift in this regard.

Cite this Research Publication : K. Radhika, Devika, K., Aswathi, T., Sreevidya, P., Sowmya V., and Dr. Soman K. P., “Performance Analysis of NASNet on Unconstrained Ear Recognition”, in Nature Inspired Computing for Data Science, Springer International Publishing, 2020, pp. 57-82, Springer, Cham.

Admissions Apply Now