Person identification using fusion of deep net facial features. (5th March 2021)

• Record Type: Journal Article
• Authors: Mehraj, Haider; Mir, Ajaz Hussain
• Is Part Of: International journal of innovative computing and applications. Volume 12:Number 1(2021)
• Page Start: 56
• Page End: 63
• Publication Date: 2021-03-05
• Subjects: biometrics -- DNN -- convolutional neural networks -- CNN -- Alexnet -- VGG-16 -- feature vector -- support vector machine -- SVM -- recognition -- multi-algorithm biometric system -- neural network -- deep networks; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Face base identification is the method of recognising individuals through face images having application domains like information security and surveillance system. Deep networks have proved to be successful for facial identification in which entire images are passed as input to deep-net, and the network does feature extraction as well as classification. However, such a process requires millions of images to work with and implementing the same sometimes becomes complicated and time-consuming. This paper utilises pre-trained deep networks Alexnet and VGG-16 as feature extractors in which contribution to more significant level layers are utilised as feature vectors. The feature vectors are then diminished using a combination of PCA and LDA. After the reduction in the dimensionality of highlight vectors, they are intertwined and characterised using support vector machines. The proposed framework is assessed on VIDTIMIT database, highlighting the performance as far as precision, accuracy, and recall.