ACLSDN: A Heuristic Face Recognition Framework with Adaptive Cascaded Deep Learning using Spectral Feature Selection
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 3 |
| Year of Publication : 2023 |
| Authors : Santhosh Shivaprakash, Sannangi Viswaradhya Rajashekararadhya |
10.14445/23488379/IJEEE-V10I3P109
How to Cite?
Santhosh Shivaprakash, Sannangi Viswaradhya Rajashekararadhya, "ACLSDN: A Heuristic Face Recognition Framework with Adaptive Cascaded Deep Learning using Spectral Feature Selection," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 73-93, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I3P109
Abstract:
In recent times, most of the Facial Recognition framework has shown better outcomes in a constrained environment. Due to its high usage, the FR has gained increased attention in the research field. However, it has faced various issues in the real-time application, such as facial disguise designing in facial features, temporal variations, and low-quality images. Therefore, there is a requirement for significant techniques for detecting and identifying the human face. This paper has implemented an effective FR technique. The adequate dataset relevant to the FR process is initially gathered from the standard dataset. Then, the images are pre-processed with the aid of the median filtering process to obtain the pre-processed images. Then, it is given to the three level-Discrete Wavelet Transform (DWT), which is used to attain the spectral features. Then, the features from the spectral features are optimally chosen by using the Improved Horse Herd Optimization algorithm termed (IHHO) derived from Horse Herd Optimization (HHO). Further, it is subjected to the Adaptive Cascaded framework termed ACLSDN, designed using Long Short Term Memory (LSTM) and Deep Neural Network (DNN) model. It is carried out by averaging the score attained from both the LSTM and DNN models for attaining the classified outcomes in maximized accuracy. Here, the same IHHO algorithm is used for optimizing the parameters in the LSTM and DNN frameworks. Finally, the effectiveness of the designed FR model is validated using various metrics and shows maximized accuracy value.
Keywords:
Adaptive Cascaded Long Short Term Memory, Deep Neural Network, Face Recognition, Improved Horse Herd Optimization, Three Level-Discrete Wavelet Transform.
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