A low-resolution real-time face recognition using extreme learning machine and its variants

ABSTRACT

Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of $O(P+mLN)$, justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome.

KEYWORDS:

• Extreme learning machine (ELM)
• online sequential ELM (OS-ELM)
• Viola-Jones algorithm
• histogram of oriented gradients (HOG)
• labeled faces in the wild (LFW)

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Ankit Rajpal

Ankit Rajpal is currently working as an Assistant Professor at Department of Computer Science, University of Delhi. His research interests include image and video watermarking, machine learning, and Data Mining. He has published several papers in reputed international journals and conferences.

Khushwant Sehra

Khushwant Sehra received the B.Tech. degree in electronics from the University of Delhi, New Delhi, India, in 2017, and the M.Tech. degree in electronics and communication engineering from the University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi, in 2019. He is currently working as a Research Scholar with the Department of Electronic Science, University of Delhi. His research interests include modeling, simulation, and fabrication of GaN-based HEMT devices. He has also worked on image processing, including digital image watermarking and development of facial recognition systems for uncontrolled environments.

Anurag Mishra

Anurag Mishra is currently working as a Professor at Department of Electronics, Deen Dayal Upadhyaya College, University of Delhi. He is actively involved in research in information security and digital watermarking of images and video in particular, intelligent systems employed for image processing using soft computing techniques such as artificial neural networks, fuzzy systems, support vector machines, and extreme learning machines.

Girija Chetty

Girija Chetty received her Bachelors and Masters degree in Electrical Engineering and Computer Science from India, and PhD in Information Sciences and Engineering from Australia. Currently, she is a Full Professor in Computing and Information Technology at School of Information Technology and Systems, at University of Canberra, Australia. Her research interests are in the area of multimodal systems, computer vision, pattern recognition, data mining, and medical image computing. She has published extensively with more than 200 fully refereed publications in several invited book chapters, edited books, high quality conference and journals, and she is in the editorial boards, technical review committees and regular reviewer for several IEEE, Elsevier and IET journals in the area related to her research interests.