A novel robust feature extraction with GSO-optimized extreme learning for age-invariant face recognition

ABSTRACT

This paper presents a novel age function modelling technique on the basis of the fusion of local features obtained by local texture descriptors. Initially, image normalization is performed and a feature extraction process is carried out. The age estimation performances of new texture descriptors Local Phase Quantization, Weber Local Descriptor and the familiar texture descriptor Local Binary Patterns, which are not examined thoroughly for age estimation modelling, are analysed in this paper. Then the feature fusion process is taken place for investigating the age estimation precisions of various concatenation of the local texture descriptors. By using PCA, dimensionality reduction is implemented for reducing the dimensions of the images. Extreme Learning Machine (ELM) classifier is applied to evaluate the output images for the corresponding input images. Because of the mild optimization restrictions, ELM can be simple for execution and normally attains the finer generalization performance. The outcomes display that, when compared with the earlier techniques, the age function modelling accuracy of the developed system is better.

KEYWORDS:

• Age invariant face recognition
• principal component analysis
• extreme learning machine
• galactic swarm optimization

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Sonu Agrawal received his M.Tech (Gold Medalist) degree in Computer Technology from National Institute of Technology (NIT) Raipur, India in 2008. He is pursuing Ph.D. from CSVTU, Bhilai. He has fifteen years long experience in the field of teaching. He is working as an Assistant Professor in Computer Science and Engineering, SSGI Bhilai, since 2004. His research areas are Image Processing, Face Recognition and its enhancement. His research work has been published in many national and international journals.

Dr. Sushil Kumar received his Ph.D. in Electrical Engineering from Pt. Pt. Ravishankar Shukla University, Raipur. Presently working as Director at Shri Rawatpura Sarkar Institute of Technology, Raipur. He has more than Twenty years long experience in the field of teaching and research. He has delivered a lot of expert lectures, chaired number of conferences and inducted with various professional bodies. His research work has been published in many national and international journals and conferences.

Dr. Sanjay Kumar received his Ph.D. in Computer Science & Engg from Ravishankar Shukla University, Raipur in 2005. Presently working as Professor, School of Studies in Computer Science & IT at Pt. Ravishankar Shukla University, Raipur. He has more than Twenty five years long experience in the field of teaching and research. His research area includes advanced networking, parallel computing and image process. He has delivered a lot of expert lectures, chaired number of conferences and inducted with various professional bodies. His research work has been published in many national and international journals and conferences.

Dr. (Mrs.) Ani Thomas is Professor and Head of the Department of Information Technology in Bhilai Institute of Technology, Durg. She obtained her Masters degree in Computer Applications from Govt Engg. College, Raipur and PhD degree in 2013 from Chhattisgarh Swami Vivekananda University. Dr. Thomas is Researcher focusing her research in exploring text mining techniques for machine learning. She has many reputed publications in her credit in national/international journals and Conferences. She is nominated as a DRC member of the Institute and member of the Board of Studies, Examination Committee and syllabus formation committee at CSVTU.