Evaluation of relevance vector machine classifier for a real-time face recognition system

Karthik, H S; Manikandan, J

doi:10.1109/icce-asia.2017.8307832

Cited by 16 publications

(3 citation statements)

References 12 publications

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“…To evaluate the system performance designed, AT&T's face dataset is first regarded, accompanied by performance review using real-time face inputs from the system camera. Using the new method, 81.25 î 97.00% recognition accuracy was achieved, as well as the proposed architecture could be easily added for numerous other pattern recognition systems as well [8].…”

Section: Karthik Et Al(2017)mentioning

confidence: 99%

A Review Study on Twins Face Identification Using LBP Technique

Chaurasiya¹

2022

IJSREM

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A user's identify & emotional condition can be inferred a great deal from their face. Face recognition affects vital applications in numerous fields, including recognition for police departments, identification for banking and security system access, and identifying information, amongst many others. It is a fascinating and difficult topic. The three key components of our study project are face depiction, feature extraction, & classification. The subsequent methods for identification and recognition are determined by the face recognition, which represents how to model a face. In the feature extraction stage, the facial image's most beneficial & distinctive traits are retrieved. The face image is compared to database pictures throughout the classification process. This paper concludes that the we empirically evaluate face recognition which considers both shape and texture information to represent face images based on Local Binary Patterns for person- independent face recognition. The face area is first divided into small regions from which Local Binary Patterns (LBP) are extracted and concatenated into a single feature vector. This feature vector forms an efficient representation of the face and is used to measure similarities between images. Keywords: Face recognition, local binary pattern (LBP), feature extraction, classification.

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Section: Karthik Et Al(2017)mentioning

confidence: 99%

A Review Study on Twins Face Identification Using LBP Technique

Chaurasiya¹

2022

IJSREM

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“…Karthik and Manikandan [9] proposed a Relevance Vector Machine (RVM) classifier to solve the face recognition in a real time system. First, the face images were processed by using Viola Jones algorithms to perform face detection and capture the face in the camera frame.…”

Section: Dnn Face Detector Inmentioning

confidence: 99%

Masked Face Recognition Attendance System Using A Modified Convolutional Neural Network

How

Tan

Liew³

2022

IJORAS

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In this paper, a masked face recognition based attendance system is developed by modifying a version of convolutional neural network (CNN). In this regard, a Support Vector Machine is integrated in the CNN to replace its original Softmax classifier to perform the task. The performance of the modified CNN in recognizing masked faces in a 5-fold cross validation was compared that of other CNNs. The experimental results show high effectiveness of the proposed CNN (i.e. 98.92%) in recognizing masked faces for recording attendance.

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“…A variety of combinations of feature extraction and classifier algorithms have been utilized in the design of these systems. For instance, some systems employ Histogram of Gradients (HOG) and Support Vector Machine Classifier (SVM), 6 while others utilize HOG with Relevance Vector Machine (RVM) Classifier, 7 or Principal Component Analysis (PCA) with SVM. 8 Convolutional Neural Network (CNN), a deep learning algorithm, is often recommended for applications involving images because it integrates both feature extraction and classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Real-time face recognition system at the edge

Ozen,

Alim,

Okcu

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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Face recognition (FR) technology has gained widespread popularity due to its diverse utility and broad range of applications. It is extensively used in various domains, including information security, access control, and surveillance. Achieving better real-time face detection (FD) performance can be challenging, especially when running multiple algorithms that require both high accuracy and swift execution (high frame rate) into embedded System on Chips (SoC). In this study, a comprehensive methodology and system implementation are proposed for concurrent face detection, landmark extraction, quality assessment, and face recognition directly at the edge, without relying on external resources. The approach integrates cutting-edge techniques, including the utilization of the Extended YOLO model for face detection and the ArcFace model for feature extraction, optimized for deployment on embedded devices. By leveraging these models alongside a dedicated recognition database and efficient software architecture, the system achieves remarkable accuracy and real-time processing capabilities. Critical aspects of the methodology involve tailoring model optimization for SoC environments, specifically focusing on the YOLO face detection model and the ArcFace feature extraction model. These optimizations aim to enhance computational efficiency while preserving accuracy. Furthermore, efficient software architecture plays a crucial role, allowing for the seamless integration of multiple components on embedded devices. Optimization techniques are employed to minimize overhead and maximize performance, ensuring real-time processing capabilities. By offering a detailed framework and implementation strategy, this research contributes significantly to the development of a high-performance, highly accurate real-time face recognition system optimized for embedded devices.

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Evaluation of relevance vector machine classifier for a real-time face recognition system

Cited by 16 publications

References 12 publications

A Review Study on Twins Face Identification Using LBP Technique

A Review Study on Twins Face Identification Using LBP Technique

Masked Face Recognition Attendance System Using A Modified Convolutional Neural Network

Real-time face recognition system at the edge

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