On importance of nose for face tracking

Gorodnichy, Dmitry O.

doi:10.1109/afgr.2002.1004153

Cited by 50 publications

(32 citation statements)

References 14 publications

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“…According to Gorodnichy the nose is the best facial feature to be tracked because it is clearly visible when the head is moving [13]. More precisely, the tip of the nose can often be approximated by a sphere.…”

Section: Nose Detection and 3d Reconstructionmentioning

confidence: 99%

COBVIS-D: A Computer Vision System for Describing the Cephalo-Ocular Behavior of Drivers in a Driving Simulator

Beauchemin

Varcheie

Gagnon

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper describes current research combining computer vision, virtual reality and kinesiology for analyzing the cephalo-ocular behavior of drivers in realistic driving contexts. The different components of the system are described and results are provided for each one. The ultimate goal of the system is to achieve automatic analysis of drivers' behavior in order to design training programs tailored to their driving habits.

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Section: Nose Detection and 3d Reconstructionmentioning

confidence: 99%

COBVIS-D: A Computer Vision System for Describing the Cephalo-Ocular Behavior of Drivers in a Driving Simulator

Beauchemin

Varcheie

Gagnon

et al. 2009

Lecture Notes in Computer Science

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“…Instead of recognizing every frame, only a set of key frames that are of good quality or suitable for recognition are selected based on several heuristics. Several approaches to select good quality frames were proposed including relative positions of eyes and nose [9], robust statistics to filter out noisy face images [10], etc. Once the key frames are identified for every track, majority voting schemes are used to finally label the entire video.…”

Section: A Related Workmentioning

confidence: 99%

Face Recognition in Videos by Label Propagation

Kumar

Namboodiri

Jawahar

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-We consider the problem of automatic identification of faces in videos such as movies, given a dictionary of known faces from a public or an alternate database. This has applications in video indexing, content based search, surveillance, and real time recognition on wearable computers. We propose a two stage approach for this problem. First, we recognize the faces in a video using a sparse representation framework using l1-minimization and select a few key-frames based on a robust confidence measure. We then use transductive learning to propagate the labels from the key-frames to the remaining frames by incorporating constraints simultaneously in temporal and feature spaces. This is in contrast to some of the previous approaches where every test frame/track is identified independently, ignoring the correlation between the faces in video tracks. Having a few key frames belonging to few subjects for label propagation rather than a large dictionary of actors reduces the amount of confusion. We evaluate the performance of our algorithm on Movie Trailer face dataset and five movie clips, and achieve a significant improvement in labeling accuracy compared to previous approaches.

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“…Method Key-frame based Approaches [90], [40], [47], [114], [100], [17], [115], [31], [78], [85], [98], [101], [118] Temporal Model based Approaches [74], [73], [72], [75], [18], [24], [67], [69], [68], [122], [120], [123], [121], [64], [65], [66], [79], [55], [2], [43], [50], [49] Image-Set Matching based Approaches Statistical model-based [93], [4], [96], [7], [10], [6], [9] Mutual subspace-based [110], [90], [35], [82], [108], [56], [57], [5]<...>…”

Section: Categorymentioning

confidence: 99%

“…In [40], face recognition is performed based on tracking the nose and eyes. Their locations are used to decide whether the face is suitable for recognition.…”

Section: Categorymentioning

confidence: 99%

Face Recognition and Retrieval in Video

Shan¹

2010

Video Search and Mining

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Abstract. Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.

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On importance of nose for face tracking

Cited by 50 publications

References 14 publications

COBVIS-D: A Computer Vision System for Describing the Cephalo-Ocular Behavior of Drivers in a Driving Simulator

COBVIS-D: A Computer Vision System for Describing the Cephalo-Ocular Behavior of Drivers in a Driving Simulator

Face Recognition in Videos by Label Propagation

Face Recognition and Retrieval in Video

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