A Kind of Human Face Region Detection and Recognition Method Based on Chrominance Information Characteristics

Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for many applications. In this chapter, the authors introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on PCH but focuses on the human body movements over time. They propose a modification of the conventional PCH that entails the calculation of two probabilistic maps based on human face and body detection, respectively. These HC-PCH features are used as input to an HMM-based classification framework, which exploits redundant information from multiple streams by employing sophisticated fusion methods, resulting in enhanced activity recognition rates.

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Color Images Illumination Compensation Method for Human Face Region Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.678.162 ◽

2014 ◽

Vol 678 ◽

pp. 162-165 ◽

Cited By ~ 1

Author(s):

Yang Yu ◽

Xiao Bin Li ◽

Hai Yan Sun

Keyword(s):

Color Images ◽

Compensation Method ◽

Detection Accuracy ◽

Human Face ◽

Light Conditions ◽

Illumination Compensation ◽

Facial Region ◽

Region Detection ◽

Face Region ◽

California Institute Of Technology

Facial region detection has broad application prospects, but existing human face region detection methods have some rigorous requirements for the light conditions. Error detection about face area caused by the poor light conditions has a great bad effect on the follow-up processing, such as face recognition, fatigue degree evaluation based on visual. So face region detection in complex lighting conditions has always been the difficult problem. Therefore a self-adaptive illumination compensation method for color images has been proposed. Select the color face images database of the California Institute of Technology to test the method dealing with the face region detection by original image and the image after illumination compensation. In the simulation experiment, the method of Illumination compensation can effectively improve the detection accuracy. Lay the foundation for driver fatigue detection based on visual.

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Human Face Region Detection Driving Activity Recognition in Video

Face Recognition in Adverse Conditions - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-5966-7.ch015 ◽

2014 ◽

pp. 316-335

Author(s):

Anastasios Doulamis ◽

Athanasios Voulodimos ◽

Theodora Varvarigou

Keyword(s):

Activity Recognition ◽

Human Face ◽

Multiple Streams ◽

Body Movements ◽

Video Signals ◽

Region Detection ◽

Face Region ◽

Classification Framework ◽

Fusion Methods ◽

Tremendous Impact

Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for many applications. In this chapter, the authors introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on PCH but focuses on the human body movements over time. They propose a modification of the conventional PCH that entails the calculation of two probabilistic maps based on human face and body detection, respectively. These HC-PCH features are used as input to an HMM-based classification framework, which exploits redundant information from multiple streams by employing sophisticated fusion methods, resulting in enhanced activity recognition rates.

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Proficient Masked Face Recognition Method Using Deep Learning Convolution Neural Network in Covid-19 Pandemic

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2021.15.189 ◽

2021 ◽

Vol 15 ◽

pp. 1751-1758

Author(s):

Saeed A. Awan ◽

Syed Asif Ali ◽

Imtiaz , Hussain ◽

Basit Hassan ◽

Syed Muhammad Ashfaq Ashraf

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recognition Performance ◽

Human Face ◽

Effective Solution ◽

Feature Maps ◽

Recognition Method ◽

Face Region ◽

A Minor ◽

Fully Connected

The COVID-19 pandemic is an incomparable disaster triggering massive fatalities and security glitches. Under the pressure of these black clouds public frequently wear masks as safeguard to their lives. Facial Recognition becomes a challenge because significant portion of human face is hidden behind mask. Primarily researchers focus to derive up with recommendations to tackle this problem through prompt and effective solution in this COVID-19 pandemic. This paper presents a trustworthy method to for the recognition of masked faces on un-occluded and deep learning-based features. The first stage is to capture the non-obstructed face region. Then we extract the most significant features from the attained regions (forehead and eye) through pre-trained deep learning CNN. Bag-of- word paradigm to has been applied to the feature maps to quantize them and to get a minor illustration comparing to the CNN’s fully connected layer. In the end a Multilayer Perceptron has been used for classification. High recognition performance with significant accuracy is seen in experimental results.

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