Face Recognition Using Deep Convolutional Network and One-shot Learning
|International Journal of Computer Science and Engineering|
|© 2020 by SSRG - IJCSE Journal|
|Volume 7 Issue 4|
|Year of Publication : 2020|
|Authors : Joyassree Sen, Bappa Sarkar, Mst. Ashrafunnahar Hena, Md. Hafizur Rahman|
How to Cite?
Joyassree Sen, Bappa Sarkar, Mst. Ashrafunnahar Hena, Md. Hafizur Rahman, "Face Recognition Using Deep Convolutional Network and One-shot Learning," SSRG International Journal of Computer Science and Engineering , vol. 7, no. 4, pp. 23-29, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I4P107
Among the most successful application of images analysis and understanding, face recognition has recently received significant attention, especially during the past few years. Facial recognition technology (FRT) has emerged as an attractive solution to address many contemporary needs for identity and verification of identity claims. Face recognition is the identification of humans by the unique characteristics of their faces. FRT technology is the least intrusive and fastest bio-metric technology. It works with the most obvious individual identifier for the human face. With increasing security needs and with the advancement in technology extracting information has become much simpler. The system proposed in this paper uses the power of Convolution Neural Network (CNN) to encode the face and produce a vector matrix. Then we use tripled loss function to calculate the distance between input and trained image to predict the face.
CNN, FRT, ANN, Machine learning, Conv
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