Local Transform Features and Hybridization for Accurate Face and Human Detection

By Bongjin Jun, Inho Choi, Daijin Kim. 2013

156 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Fei Han
2. William A. Hoff
3. Brian Reily
4. Stefanos P. Zafeiriou
5. Jia Li
6. Hao Zhang
7. Dacheng Tao
8. G. Wang

Citations in the corpus

• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “As an integral component of reasoning systems, approaches to construct human representations have been widely used in a variety of real-world applications, including video analysis [1], surveillance [2], robotics [3], humanmachine interaction [4], augmented and virtual reality [5], assistive living ”
• Depth-based face recognition using local quantized patterns adapted for range data (2014, methodology): “Regarding the state-of-the-art techniques that use color imagery, popular feature extraction techniques to characterize people faces are: the Scale Invariant Feature Transform (SIFT) [5], the Histogram of Oriented Gradients (HOG) [6], and the Local Binary Pattern (LBP) [7].”
• SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2 (2020, unknown): “They are used to facilitate self-focusing (Huang, Ai, Li, & Lao, 2007), the interaction be-tween humans and computers (Jun, Choi, & Kim, 2012), and managing image database (Ge, Li, Ye, & Luo, 2017).”
• A survey on face detection in the wild: Past, present and future (2015, methodology): “Featuresforface/objectdetection. featuresand RotatedHaar-likefeatures[63] itsvariations Rectangularfeatureswithstructure[59, 64] Haar-likefeaturesonmotionfiltered image[65] Pixel-based Pixelpairs[78] features Controlpointset[79] Binarized Modifiedcensustransform[69] features LBPfeatures[71,72] Local”
• Detecting Masked Faces in the Wild with LLE-CNNs (2017, background): “Due to the great success of these face detectors, some of them have been integrated into applications so as to facilitate auto-focusing [10], human computer interaction [12] and image database management [27].”
• Human Action Recognition With Video Data: Research and Evaluation Challenges (2014, methodology): “method able to withstand pose, shape, and illumination changes [64] compared with using the entire global features.”