Deep Sparse Representation Classifier for facial recognition and detection system

Cheng, Eric-Juwei; Chou, Kuang-Pen; Rajora, Shantanu; Jin, Bo-Hao; Tanveer, M.; Lin, Cheng‐Han; Young, Kuu‐Young; Lin, Wen‐Chieh; Prasad, Mukesh

doi:10.1016/j.patrec.2019.03.006

Cited by 83 publications

(29 citation statements)

References 14 publications

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“…CNNs have been applied to a variety of computer vision tasks, such as image classification [18], object detection [10], semantic segmentation [19], facial recognition [20], and hyperspectral image recognition [21], among others. Specifically, in the object detection field, networks such as Faster R-CNN [10], R-FCN [8], YOLO [22], and SSD [9] have achieved high accuracy and hence attracted a great deal of attention from researchers.…”

Section: Object Detection Using Convolutional Neural Networkmentioning

confidence: 99%

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module

Wang

Xie

Yang

et al. 2020

Sensors

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Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessary for administrators to identify the people that do not wear hardhats and send out alarms to them. As manual inspection is labor-intensive and expensive, it is ideal to handle this issue by a real-time automatic detector. As such, in this paper, we present an end-to-end convolutional neural network to solve the problem of detecting if workers are wearing hardhats. The proposed method focuses on localizing a person’s head and deciding whether they are wearing a hardhat. The MobileNet model is employed as the backbone network, which allows the detector to run in real time. A top-down module is leveraged to enhance the feature-extraction process. Finally, heads with and without hardhats are detected on multi-scale features using a residual-block-based prediction module. Experimental results on a dataset that we have established show that the proposed method could produce an average precision of 87.4%/89.4% at 62 frames per second for detecting people without/with a hardhat worn on the head.

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Section: Object Detection Using Convolutional Neural Networkmentioning

confidence: 99%

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module

Wang

Xie

Yang

et al. 2020

Sensors

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“…Recognizing the identity of people in video accurately and quickly is very important for video search and video surveillance. Schofield et al proposed a deep convolution neural network method, which could automatically detect, track and record human faces in video, and could be used to study the animal behavior [33] [34].…”

Section: Deep Learningmentioning

confidence: 99%

A Review of Face Recognition Technology

et al. 2020

IEEE Access

223

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Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the recognition equipment automatically processes the images. The paper introduces the related researches of face recognition from different perspectives. The paper describes the development stages and the related technologies of face recognition. We introduce the research of face recognition for real conditions, and we introduce the general evaluation standards and the general databases of face recognition. We give a forward-looking view of face recognition. Face recognition has become the future development direction and has many potential application prospects.

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“…The sparse representation which assumes the test sample can be linearly represented by the dictionary is robust to noise, clutter, and occlusion. It has been widely used in face recognition and object tracking because it is robust to feature loss and clutter [35], [36]. The orientation can reflect the relationships between neighboring pixels points and the underlying inherent structure of images.…”

Section: Sparse Representation Classificationmentioning

confidence: 99%

A Hybrid Framework Based on Warped Hierarchical Tree for Pose Estimation of Texture-Less Objects

et al. 2020

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The pose of texture-less objects is very important for intelligent manufacturing and intelligent assembly. Existing methods cannot accurately estimate pose when partial features are missing or cluttered due to shading, reflection, and occlusion. We propose a hybrid framework based on the warped hierarchical tree for pose estimation which integrates template matching and sparse representation classification in this paper. Firstly, the template is formed by the prospectively cumulated orientation feature (PCOF), which is a probabilistic representation of orientations extracted from template images. And the warped hierarchical tree can be built offline according to the parameters for projecting the 3D object and the similarity between templates. Then the online searching can be repeated through the warped hierarchical tree until the template candidates have been found. Finally, the pose corresponding to the best-fitting template can be obtained by sparse representation classification based on the dictionaries consisted of the spreading orientations of template candidate images. The experiment results show the effectiveness of our method when partial features are missing or cluttered.

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Deep Sparse Representation Classifier for facial recognition and detection system

Cited by 83 publications

References 14 publications

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module

A Review of Face Recognition Technology

A Hybrid Framework Based on Warped Hierarchical Tree for Pose Estimation of Texture-Less Objects

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