Recent Advances in Deep Learning Techniques for Face Recognition

In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 168 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp. 99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613

SignReLU neural network and its approximation ability

Deep neural networks (DNNs) have garnered significant attention in various fields of science and technology in recent years. Activation functions define how neurons in DNNs process incoming signals for them. They are essential for learning non-linear transformations and for performing diverse computations among successive neuron layers. In the last few years, researchers have investigated the approximation ability of DNNs to explain their power and success. In this paper, we explore the approximation ability of DNNs using a different activation function, called SignReLU. Our theoretical results demonstrate that SignReLU networks outperform rational and ReLU networks in terms of approximation performance. Numerical experiments are conducted comparing SignReLU with the existing activations such as ReLU, Leaky ReLU, and ELU, which illustrate the competitive practical performance of SignReLU

A novel attention-based gated recurrent unit and its efficacy in speech emotion recognition

Abstract Notwithstanding the significant advancements in the field of deep learning, the basic long short-term memory (LSTM) or Gated Recurrent Unit (GRU) units have largely remained unchanged and unexplored. There are several possibilities in advancing the state-of-art by rightly adapting and enhancing the various elements of these units. Activation functions are one such key element. In this work, we explore using diverse activation functions within GRU and bi-directional GRU (BiGRU) cells in the context of speech emotion recognition (SER). We also propose a novel Attention ReLU GRU (AR-GRU) that employs attention-based Rectified Linear Unit (AReLU) activation within GRU and BiGRU cells. We demonstrate the effectiveness of AR-GRU on one exemplary application using the recently proposed network for SER namely Interaction-Aware Attention Network (IAAN). Our proposed method utilising AR-GRU within this network yields significant performance gain and achieves an unweighted accuracy of 68.3% (2% over the baseline) and weighted accuracy of 66.9 % (2.2 % absolute over the baseline) in four class emotion recognition on the IEMOCAP database

Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization

This work aims to provide profound insights into neural networks and deep learning, focusing on their functioning, interpretability, and generalization capabilities. It explores fundamental aspects such as network architectures, activation functions, and learning algorithms, analyzing their theoretical foundations. The paper delves into the theoretical analysis of deep learning models, investigating their representational capacity, expressiveness, and convergence properties. It addresses the crucial issue of interpretability, presenting theoretical approaches for understanding the inner workings of these models. Theoretical aspects of generalization are also explored, including overfitting, regularization techniques, and generalization bounds. By advancing theoretical understanding, this paper paves the way for informed model design, improved interpretability, and enhanced generalization in neural networks and deep learning, pushing the boundaries of their application in diverse domains

Activation functions : comparison of trends in practice and research for deep learning

Deep neural networks (DNN) have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning (DL) architectures, being developed to date. To achieve this state-of-the-art (SOTA) performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the AFs for DL applications. The novelty of this paper is that it compiles the majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the SOTA research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate AF for a given application, ready for deployment. This paper is timely because majority of the research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from the literature, found in DL research to date