Journal ArticleDOI
Ranking, clustering and fusing the normalized LBP temporal facial features for face recognition in video sequences
P. Ithaya Rani,T. Hari Prasath +1 more
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TLDR
A novel approach for recognizing faces in videos with high recognition rate that embeds diverse intra-personal variations such as poses, expressions and facilitates in matching two videos with large variations and exhibits significant performance improvement when compared with the existing techniques.Abstract:
This paper proposes a novel approach for recognizing faces in videos with high recognition rate. Initially, the feature vector based on Normalized Local Binary Patterns is obtained for the face region. A set of training and testing videos are used in this face recognition procedure. Each frame in the query video is matched with the signature of the faces in the database using Euclidean distance and a rank list is formed. Each ranked list is clustered and its reliability is analyzed for re-ranking. Multiple re-ranked lists of the query video is fused together to form a video signature. This video signature embeds diverse intra-personal variations such as poses, expressions and facilitates in matching two videos with large variations. For matching two videos, their composite ranked lists are compared using a Kendall Tau distance measure. The developed methods are deployed on the YouTube and ChokePoint videos, and they exhibit significant performance improvement owing to their novel approach when compared with the existing techniques.read more
Citations
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Patent
Face image processing method and device and computer readable storage medium
TL;DR: In this article, a face image processing method and device and a computer readable storage medium is described, and the method comprises the steps: obtaining a to-be-processed image, and enabling the to be processed image to comprise a head image; carrying out face vectorization on the face image to obtain a face feature vector corresponding to the head image.
Book ChapterDOI
Detection of Human Faces in Video Sequences Using Mean of GLBP Signatures
TL;DR: In this paper, a spontaneous and vigorous method to identify the location of face area using recently developed You Tube Video face database is presented. But the method is not suitable for head poses and is not sturdy to variations in illumination, appearance and noisy images.
References
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Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Book ChapterDOI
Face Recognition with Local Binary Patterns
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Journal ArticleDOI
Score normalization in multimodal biometric systems
TL;DR: Study of the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user found that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods.
Proceedings ArticleDOI
Face recognition in unconstrained videos with matched background similarity
Lior Wolf,Tal Hassner,Itay Maoz +2 more
TL;DR: A comprehensive database of labeled videos of faces in challenging, uncontrolled conditions, the ‘YouTube Faces’ database, along with benchmark, pair-matching tests are presented and a novel set-to-set similarity measure, the Matched Background Similarity (MBGS), is described.
Proceedings ArticleDOI
Face tracking and recognition with visual constraints in real-world videos
TL;DR: This work addresses the problem of tracking and recognizing faces in real-world, noisy videos using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting and introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework.