Abstract

An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.

Highlights

  • Face recognition from videos is still a challenging problem

  • An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and the information set features

  • The algorithm is tested at different window sizes and the best results are obtained with features extracted from window of 35

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Summary

Introduction

One source of videos is CCT cameras installed wherever the thefts and criminal activities are expected. Earlier the passport photo used to be the only identity for travelers, but secrete cameras at airports keep a strict vigil on their activities as well. They record real time videos during the immigration checks. Their applications are furthered in detecting the poses of people, tracking the objects and activities

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