Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Yang, Ziyuan; Li, Jing; Min, Weidong; Wang, Qi

doi:10.3390/app9204344

Cited by 11 publications

(6 citation statements)

References 48 publications

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“…In recent years, deep learning has achieved tremendous development and remarkable achievements in various fields of computer vision [35][36][37]. Zhong and Zhu [38] designed a new loss function for palmprint recognition, which can make the distance distribution of Inter-class more concentrated, while the distance distribution of intra-class more dispersed.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Presentation Attacks in Palmprint Recognition Systems

Sun¹,

Wang²

2022

J Multimed Inf Syst

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Background: A presentation attack places the printed image or displayed video at the front of the sensor to deceive the biometric recognition system. Usually, presentation attackers steal a genuine user's biometric image and use it for presentation attack. In recent years, reconstruction attack and adversarial attack can generate high-quality fake images, and have high attack success rates. However, their attack rates degrade remarkably after image shooting. Methods: In order to comprehensively analyze the threat of presentation attack to palmprint recognition system, this paper makes six palmprint presentation attack datasets. The datasets were tested on texture coding-based recognition methods and deep learning-based recognition methods. Results and conclusion: The experimental results show that the presentation attack caused by the leakage of the original image has a high success rate and a great threat; while the success rates of reconstruction attack and adversarial attack decrease significantly.

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Section: Deep-learning-based Methodsmentioning

confidence: 99%

Presentation Attacks in Palmprint Recognition Systems

Sun¹,

Wang²

2022

J Multimed Inf Syst

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“…Pang et al [ 33 ] improved “you only look once” (YOLO) [ 34 ] to detect concealed objects. Yang et al [ 35 ] proposed a real-time cascaded framework to detect tiny faces. Yuan et al [ 36 ] proposed a scale-adaptive CNN to detect occluded targets and track them.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

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“…B. Ríos-Sánchez, D. Costa-da-Silva, N. Martín-Yuste and C. Sánchez-Ávila, described and evaluated two deep learning models for face recognition in terms of accuracy and size, which were designed for the applications in mobile devices and resource saving environments [5]. The 4th paper, authored by Z. Yang, J. Li, W. Min and Q. Wang, presented real-time pre-identification and cascaded detection for tiny faces to reduce background and other irrelevant information [6]. The cascade detector consisted of a two-stage convolutional neuron network to detect tiny faces in a coarse-to-fine manner.…”

Section: Facementioning

confidence: 99%