Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Babu, K. Hari; Kumar, C. Santhosh; Kannaiyaraju, C.

doi:10.32604/iasc.2022.023756

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…There are many types of DNN models which are used for different purposes. For example, deep belief networks (DBN) have been used for facial recognition [ 32 ] and crack detection [ 33 ]; stacked auto-encoder (SAE) networks have been used for object detection [ 34 ], image compression [ 35 ], and video retrieval [ 36 ]; restricted Boltzmann machines (RBM) have been used for face recognition [ 37 ], and YOLO (You Only Look Once)-based DL methods have been utilized in object-detection [ 38 ] tasks.…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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Real-Time Facial Expression Recognition: Advances, Challenges, and Future Directions

Dewi,

Gunawan,

Hastoko

et al. 2023

Vietnam J. Comp. Sci.

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Facial emotion recognition (FER) is the technology or process of identifying and interpreting human emotions based on the analysis of facial expressions. It involves using computer algorithms and machine learning techniques to detect and classify emotional states from images or videos of human faces. Further, FER plays a vital role in recognizing and understanding human emotions to better interpret someone’s feelings, intentions, and attitudes. In the present time, it is widely used in various fields such as healthcare, human–computer interaction, law enforcement, security, and beyond. FER has a wide range of practical applications across various industries including Emotion Monitoring, Adaptive Learning, and Virtual Assistants. This paper presents a comparative analysis of FER algorithms, focusing on deep learning approaches. The performance of different datasets, including FER2013, JAFFE, AffectNet, and Cohn–Kanade, is evaluated using convolutional neural networks (CNNs), deep face, attentional convolutional networks (ACNs), and deep belief networks (DBNs). Among the tested algorithms, DBNs outperformed other algorithms, reaching the highest accuracy of 98.82%. These results emphasize the effectiveness of deep learning techniques, particularly DBNs, in FER. Additionally, outlining the advantages and disadvantages of current research on facial emotion identification might direct future research efforts in the direction of the most profitable directions.

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Revisiting face detection: Supercharging Viola-Jones with particle swarm optimization for enhanced performance

Mohana,

Subashini,

Shukla

2024

IFS

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In recent years, face detection has emerged as a prominent research field within Computer Vision (CV) and Deep Learning. Detecting faces in images and video sequences remains a challenging task due to various factors such as pose variation, varying illumination, occlusion, and scale differences. Despite the development of numerous face detection algorithms in deep learning, the Viola-Jones algorithm, with its simple yet effective approach, continues to be widely used in real-time camera applications. The conventional Viola-Jones algorithm employs AdaBoost for classifying faces in images and videos. The challenge lies in working with cluttered real-time facial images. AdaBoost needs to search through all possible thresholds for all samples to find the minimum training error when receiving features from Haar-like detectors. Therefore, this exhaustive search consumes significant time to discover the best threshold values and optimize feature selection to build an efficient classifier for face detection. In this paper, we propose enhancing the conventional Viola-Jones algorithm by incorporating Particle Swarm Optimization (PSO) to improve its predictive accuracy, particularly in complex face images. We leverage PSO in two key areas within the Viola-Jones framework. Firstly, PSO is employed to dynamically select optimal threshold values for feature selection, thereby improving computational efficiency. Secondly, we adapt the feature selection process using AdaBoost within the Viola-Jones algorithm, integrating PSO to identify the most discriminative features for constructing a robust classifier. Our approach significantly reduces the feature selection process time and search complexity compared to the traditional algorithm, particularly in challenging environments. We evaluated our proposed method on a comprehensive face detection benchmark dataset, achieving impressive results, including an average true positive rate of 98.73% and a 2.1% higher average prediction accuracy when compared against both the conventional Viola-Jones approach and contemporary state-of-the-art methods.

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Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Cited by 3 publications

References 24 publications

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Real-Time Facial Expression Recognition: Advances, Challenges, and Future Directions

Revisiting face detection: Supercharging Viola-Jones with particle swarm optimization for enhanced performance

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