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Facial recognition system for security access control

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Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties lead to a key challenge of large-scale Facial Expression Recognition (FER) in deep learning era. To address this problem, this paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with 88.14% on RAF-DB, 60.23% on AffectNet, and 89.35% on FERPlus.
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Inferring scene geometry and camera motion from a stream of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. The authors have developed a factorization method that can overcome this difficulty by recovering shape and motion under orthography without computing depth as an intermediate step. An image stream can be represented by the measurement matrix of the image coordinates of points tracked through frames. The authors show that under orthographic projection this matrix is of rank 3. Based on this observation, the factorization method uses the singular-value decomposition technique to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively. Two of the three translation components are computed in a preprocessing stage. The method can also handle and obtain a full solution from a partially filled-in measurement matrix that may result from occlusions or tracking failures. The method gives accurate results, and does not introduce smoothing in either shape or motion. The authors demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions
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We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
New method for subject identification based on palm print
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Mejía, H. I., Chiclayo, R. A., Florez, N., Tuesta, V. and Forero, M. G., "New method for subject identification based on palm print", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115100L (21 August 2020); <>
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Wang, Y., Li, Y., Song, Y. and Rong, X. "The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition." Appl. Sci. 2020, 10, 1897. <>
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Geitgey, A. "Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning. Medium", Medium,14 July 2016, < > (14 July 2016)
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Sehgal, A., "Part 3 -Lucas-Kanade Optical Flow", Medium, 17 May 2020, <>, (2020)
How to Detect Mouth Open for Face Login
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Xie, P., "How to Detect Mouth Open for Face Login", Towards data science,8 October 2019, <>, (8 October 2019)
Real-Time Eye Blink Detection using Facial Landmarks
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Soukupová, T. and Cech, J., "Real-Time Eye Blink Detection using Facial Landmarks.", Semantic scholar, 2016, < 4fa1ba3531219ca8c39d8749160faf1a877f2ced>, (2016)
Eye blink detection with OpenCV, Python, and dlib
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Rosebrock, A., "Eye blink detection with OpenCV, Python, and dlib", Pyimagesearch, 24 April 2017, <>, (24 April 2017)
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Rosebrock, A., "Liveness Detection with OpenCV", Pyimagesearch, 11 March 2019, <>, (11 March 2019)