Biologically Inspired Face Recognition: Toward Pose-Invariance

Wi, Noel Tay Nuo; Loo, Chu Kiong; Chockalingam, L.

doi:10.1142/s0129065712500293

Cited by 4 publications

(1 citation statement)

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“…It should be mentioned that many researchers resort to face recognition for identifying pedestrians from images [6,38,41]. The recognition result in controlled environments can approach a human's performance; A probability density or an ordinary function x…”

Section: Introductionmentioning

confidence: 99%

Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Goodman

et al. 2013

ICA

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A Support Vector Machine (SVM) is an effective method for pedestrian detection applications; however, performance of an SVM is closely related to the samples that are used to train it. An SVM classifier trained by samples from well-known pedestrian datasets such as INRIA and MIT is observed to have limited detection capability in practical environments. In this paper, a statistical background-foreground extraction approach is proposed that autonomously generates samples containing pedestrians in real scenes, in order to diversify the basic training set of the SVM. Comparative experiments have shown that the SVM classifier's discriminability and adaptability to a new scene are greatly enhanced by utilizing extracted samples from that scene in the training stage. Here, a pedestrian tracker that combines a Camshift tracker and a Kalman filter is adjoined to the pedestrian classifier; the tracker is proved to be robust against pose and scale changes, abrupt direction of motion changes, and occlusions, in several test scenes.

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Section: Introductionmentioning

confidence: 99%

Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Goodman

et al. 2013

ICA

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An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fitting Model

Zhang

Yan

et al. 2023

Machine Learning for Cyber Security

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Multimodal Integration of Micro-Doppler Sonar and Auditory Signals for Behavior Classification With Convolutional Networks

Durá-Bernal

Garreau

Georgiou

et al. 2013

Int. J. Neur. Syst.

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The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance.

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Biologically Inspired Face Recognition: Toward Pose-Invariance

Cited by 4 publications

References 102 publications

Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fitting Model

Multimodal Integration of Micro-Doppler Sonar and Auditory Signals for Behavior Classification With Convolutional Networks

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