Scheirer, Walter; Flynn, Patrick; Ding, Changxing; Guo, Guodong; Štruc, Vitomir; Jazaery, Mohamad Al; Dobrišek, Simon; Grm, Klemen; Tao, Dacheng; Zhu, Yu; Brogan, Joel; Banerjee, Sandipan; Bharati, Aparna; Webster, Brandon Richard
Report on the BTAS 2016 Video Person Recognition Evaluation Inproceedings
In: Proceedings of the IEEE International Conference on Biometrics: Theory, Applications ans Systems (BTAS), IEEE, 2016.
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Pointand- Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1,401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. An additional experiment required algorithms to recognize people in videos from the Video Database of Moving Faces and People (VDMFP). There were 958 videos in this experiment of 297 subjects. Four groups from around the world participated in the evaluation. The top verification rate for PaSC from this evaluation is 0:98 at a false accept rate of 0:01 — a remarkable advancement in performance from the competition held at FG 2015.
Štruc, Vitomir; Križaj, Janez; Dobrišek, Simon
Modest face recognition Inproceedings
In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), pp. 1–6, IEEE, 2015.
The facial imagery usually at the disposal for forensics investigations is commonly of a poor quality due to the unconstrained settings in which it was acquired. The captured faces are typically non-frontal, partially occluded and of a low resolution, which makes the recognition task extremely difficult. In this paper we try to address this problem by presenting a novel framework for face recognition that combines diverse features sets (Gabor features, local binary patterns, local phase quantization features and pixel intensities), probabilistic linear discriminant analysis (PLDA) and data fusion based on linear logistic regression. With the proposed framework a matching score for the given pair of probe and target images is produced by applying PLDA on each of the four feature sets independently - producing a (partial) matching score for each of the PLDA-based feature vectors - and then combining the partial matching results at the score level to generate a single matching score for recognition. We make two main contributions in the paper: i) we introduce a novel framework for face recognition that relies on probabilistic MOdels of Diverse fEature SeTs (MODEST) to facilitate the recognition process and ii) benchmark it against the existing state-of-the-art. We demonstrate the feasibility of our MODEST framework on the FRGCv2 and PaSC databases and present comparative results with the state-of-the-art recognition techniques, which demonstrate the efficacy of our framework.
Beveridge, Ross; Zhang, Hao; Draper, Bruce A; Flynn, Patrick J; Feng, Zhenhua; Huber, Patrik; Kittler, Josef; Huang, Zhiwu; Li, Shaoxin; Li, Yan; Štruc, Vitomir; Križaj, Janez; others,
In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG), pp. 1–8, IEEE 2015.
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world participated in the evaluation. The video handheld experiment was included in the International Joint Conference on Biometrics (IJCB) 2014 Handheld Video Face and Person Recognition Competition. The top verification rate from this evaluation is double that of the top performer in the IJCB competition. Analysis shows that the factor most effecting algorithm performance is the combination of location and action: where the video was acquired and what the person was doing.
Dobrišek, Simon; Štruc, Vitomir; Križaj, Janez; Mihelič, France
In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): BWild 2015, pp. 1–6, IEEE 2015.
The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image using a battery of Gabor filters, then reduces the dimensionality of the extracted feature vector by projecting it into a low-dimensional subspace and finally produces a representation suitable for identity inference by applying PLDA to the projected feature vector. The proposed approach extends the popular Gabor-Fisher Classifier (GFC) to a probabilistic setting and thus improves on the generalization capabilities of the GFC method. The PGFC technique is assessed in face verification experiments on the Point and Shoot Face Recognition Challenge (PaSC) database, which features real-world videos of subjects performing everyday tasks. Experimental results on this challenging database show the feasibility of the proposed approach, which improves on the best results on this database reported in the literature by the time of writing.
Beveridge, Ross; Zhang, Hao; Flynn, Patrick; Lee, Yooyoung; Liong, Venice Erin; Lu, Jiwen; de Angeloni, Marcus Assis; de Pereira, Tiago Freitas; Li, Haoxiang; Hua, Gang; Štruc, Vitomir; Križaj, Janez; Phillips, Jonathon
In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8, IEEE 2014.
The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing the query video to a set of target videos. Both target and query videos are drawn from the same pool of 1401 videos. In the Still-to-video Experiment the person in a query video is to be recognized by comparing the query video to a larger target set consisting of still images. Algorithm performance is characterized by verification rate at a false accept rate of 0:01 and associated receiver operating characteristic (ROC) curves. Participants were provided eye coordinates for video frames. Results were submitted by 4 institutions: (i) Advanced Digital Science Center, Singapore; (ii) CPqD, Brasil; (iii) Stevens Institute of Technology, USA; and (iv) University of Ljubljana, Slovenia. Most competitors demonstrated video face recognition performance superior to the baseline provided with PaSC. The results represent the best performance to date on the handheld video portion of the PaSC.