Event-based Dynamic Face Detection and Tracking Based on Activity

We present the first purely event-based approach for face detection using an ATIS, a neuromorphic camera. We look for pairs of blinking eyes by comparing local activity across the input frame to a predefined range, which is defined by the number of events per second in a specific location. If within range, the signal is checked for additional constraints such as duration, synchronicity and distance between the eyes. After a valid blink is registered, we await a second blink in the same spot to initiate Gaussian trackers above the eyes. Based on their position, a bounding box around the estimated outlines of the face is drawn. The face can then be tracked until it is occluded.

[1]  Shuo Yang,et al.  WIDER FACE: A Face Detection Benchmark , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Chiara Bartolozzi,et al.  An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Eugenio Culurciello,et al.  Activity-driven, event-based vision sensors , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[4]  Ryad Benosman,et al.  Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[6]  Gregory Cohen,et al.  Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades , 2015, Front. Neurosci..

[7]  A. Bentivoglio,et al.  Analysis of blink rate patterns in normal subjects , 1997, Movement disorders : official journal of the Movement Disorder Society.

[8]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Hong Yang,et al.  DART: Distribution Aware Retinal Transform for Event-Based Cameras , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Constantin A Rothkopf,et al.  Humans quickly learn to blink strategically in response to environmental task demands , 2018, Proceedings of the National Academy of Sciences.

[12]  Makoto Kato,et al.  Blink-related momentary activation of the default mode network while viewing videos , 2012, Proceedings of the National Academy of Sciences.

[13]  Shuo Yang,et al.  Faceness-Net: Face Detection through Deep Facial Part Responses , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Stefanos Zafeiriou,et al.  A survey on face detection in the wild: Past, present and future , 2015, Comput. Vis. Image Underst..

[15]  Garrick Orchard,et al.  HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Luc Van Gool,et al.  Face Detection without Bells and Whistles , 2014, ECCV.

[17]  Jingyu Yang,et al.  Driver Fatigue Detection: A Survey , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[18]  D. Schroeder,et al.  Blink Rate: A Possible Measure of Fatigue , 1994, Human factors.

[19]  Huaizu Jiang,et al.  Face Detection with the Faster R-CNN , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[20]  Simone Benedetto,et al.  Driver workload and eye blink duration , 2011 .