Human Face Identification using LBP and Haar-like Features for Real Time Attendance Monitoring

Rusia, Mayank Kumar; Singh, Dushyant Kumar; Ansari, M. A.

doi:10.1109/iciip47207.2019.8985867

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…However, as time went on, the idea of human-computer interaction and artificial intelligence increased the importance of emotion recognition. Researchers suggested employing local binary pattern histogram and Haar-like features with a cascade classifier to recognize a person's face in real-time movies [9], but no significant work has been conducted to determine emotions.…”

Section: Related Workmentioning

confidence: 99%

Emotion Detection Using Facial Expression Involving Occlusions and Tilt

et al. 2022

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Facial emotion recognition (FER) is an important and developing topic of research in the field of pattern recognition. The effective application of facial emotion analysis is gaining popularity in surveillance footage, expression analysis, activity recognition, home automation, computer games, stress treatment, patient observation, depression, psychoanalysis, and robotics. Robot interfaces, emotion-aware smart agent systems, and efficient human–computer interaction all benefit greatly from facial expression recognition. This has garnered attention as a key prospect in recent years. However, due to shortcomings in the presence of occlusions, fluctuations in lighting, and changes in physical appearance, research on emotion recognition has to be improved. This paper proposes a new architecture design of a convolutional neural network (CNN) for the FER system and contains five convolution layers, one fully connected layer with rectified linear unit activation function, and a SoftMax layer. Additionally, the feature map enhancement is applied to accomplish a higher detection rate and higher precision. Lastly, an application is developed that mitigates the effects of the aforementioned problems and can identify the basic expressions of human emotions, such as joy, grief, surprise, fear, contempt, anger, etc. Results indicate that the proposed CNN achieves 92.66% accuracy with mixed datasets, while the accuracy for the cross dataset is 94.94%.

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Section: Related Workmentioning

confidence: 99%

Emotion Detection Using Facial Expression Involving Occlusions and Tilt

et al. 2022

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“…We have deployed the most popular Voila-Jones method to implement face detection tasks, comprising Haar-like features, integral images, cascade classifier, and Adaboost algorithm. Haar-like features [11] are the grayscale templates that include line, center-surround, and edge features and are more similar to a human face's geometry. Integral images are utilized on facial pixels for faster feature extraction.…”

Section: Face Detection and Segmentationmentioning

confidence: 99%

“…The Hue and Saturation components reflect the chrominance information, whereas the value represents the luminance information. The three components, H, S, and V, can be derived from the RGB color model as shown in (11), (12), and (13), respectively:…”

Section: Hsv Color Spacementioning

confidence: 99%

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Rusia

Singh

2022

Cybernetics and Information Technologies

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Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.

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“…In [7] authors suggests an attendance monitoring system based on face detection and recognition using Haar-like features and local binary patterns (LBP) histogram with face identification accuracy of 81.6 percent.…”

Section: Many Work Have Targeted the Analysis Of Training Ofmentioning

confidence: 99%

Haar-features training parameters analysis in boosting based machine learning for improved face detection

Mutneja¹,

Singh²

2021

IJATEE

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Haar features have been used in most of the works in literature as key components in the task of object as well as face detection. Training of Haar features is an important step in the development of overall machine learning based face detection system. In this work, we have done investigation in the variations of a number of training parameters during AdaBoost based machine learning of Haar features with respect to size of training images. A number of observations have been drawn based on the variations of these parameters during training process. Training parameters True Detection Rate and Figure of Merit have been used as weighing parameter in formation of detection cascade for improvement in original AdaBoost framework used for machine learning of Haar features. Statistical analysis based on variations of training parameters has been done for selection of efficient learners from a large pool of available features which further are cascaded to make the strong classifiers.we have been able to achieve the maximum percentage reduction in training time of 47.20 corresponding to training images size 25x25 pixels with the help of statistical screening of Haar features prior to AdaBoost. The proposed system has been developed using training dataset from CBCL and successfully tested on facial images from datasets WIDER FACE a Face Detection Benchmark dataset, Annotated Faces in the Wild (AFW) and Face Detection Data Set and Benchmark (FDDB). and been able to achieve promising results in terms of training time computedin range of 0.31-2.83hrs, maximum detection rate 96.21% and minimum detection time16.95ms.

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Human Face Identification using LBP and Haar-like Features for Real Time Attendance Monitoring

Cited by 12 publications

References 15 publications

Emotion Detection Using Facial Expression Involving Occlusions and Tilt

Emotion Detection Using Facial Expression Involving Occlusions and Tilt

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Haar-features training parameters analysis in boosting based machine learning for improved face detection

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