Abstract
Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.
Highlights
In the past few years, the field of machine learning has undergone some major developments.One important advancement is a technique known as “deep learning” that aims to model the high-level data abstractions by employing deep networked architectures composed of multiple linear/non-linear transformations
This paper investigates the performance of the pre-trained convolution neural network (CNN) with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification
Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces
Summary
In the past few years, the field of machine learning has undergone some major developments.One important advancement is a technique known as “deep learning” that aims to model the high-level data abstractions by employing deep networked architectures composed of multiple linear/non-linear transformations. Known as deep structured learning or hierarchical learning, belongs to the family of machine learning methods which are based on understanding data representation. It has made a remarkable impact in computer vision performance previously unattainable on many tasks such as image classification and object detection. Deep learning is applied in research concerning graphical modeling, pattern recognition, signal processing [1], computer vision [2], speech recognition [3], language recognition [4,5], audio recognition [6], and face recognition (FR) [7].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
