IEEE Trans Pattern Anal Mach Intell - Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition.

Tópicos

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Resumo

The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.

Resumo Limpo

uncontrol condit realworld biometr applic pose great challeng face recognit approach unconstrain acquisit data uncoop subject may result facial scan signific pose variat along yaw axi pose variat can caus extens occlus result miss data paper novel d face recognit method propos use facial symmetri handl pose variat employ automat landmark detector estim pose detect occlud area facial scan subsequ annot face model regist fit scan fit facial symmetri use overcom challeng miss data result pose invari geometri imag unlik exist method requir frontal scan propos method perform comparison among interpos scan use waveletbas biometr signatur suitabl realworld applic requir half face visibl sensor propos method evalu use databas univers notr dame univers houston best knowledg includ challeng pose variat public avail averag rankon recognit rate propos method databas percent

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