Work

4D unconstrained real-time face recognition using a commodity depth camera

Year: 2012

Type: conference-paper

Abstract: Robust unconstrained real-time face recognition still remains a challenge today. The recent addition to the market of lightweight commodity depth sensors brings new possibilities for human-machine interaction and therefore face recognition. This article accompanies the reader through a succinct survey of the current literature on face recognition in general and 3D face recognition using depth sensors in particular. Consequent to the assessment of experiments performed using implementations of the most established algorithms, it can be concluded that the majority are biased towards qualitative performance and are lacking in speed. A novel method which uses noisy data from such a commodity sensor to build dynamic internal representations of faces is proposed. Distances to a surface normal to the face are measured in real-time and used as input to a specific type of recurrent neural network, namely long short-term memory. This enables the prediction of facial structure in linear time and also increases robustness towards partial occlusions.

Source: IEEE Conference on Industrial Electronics and Applications (ICIEA)

DOI: 10.1109/iciea.2012.6360717

Authors: Florin Schimbinschi, Marco Wiering, Rajesh Elara Mohan, Jaichandar Kulandaidaasan Sheba

Institutions: University of Groningen, Singapore University of Technology and Design, Singapore Polytechnic

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2114388147

FWCI: 0.665

Cites: 59

Cited by: 4

Related to: 10