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首页> 外文期刊>Information Forensics and Security, IEEE Transactions on >Passive Multimodal 2-D+3-D Face Recognition Using Gabor Features and Landmark Distances
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Passive Multimodal 2-D+3-D Face Recognition Using Gabor Features and Landmark Distances

机译：利用Gabor特征和地标距离的无源多峰式2-D + 3-D人脸识别

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摘要

We introduce a novel multimodal framework for face recognition based on local attributes calculated from range and portrait image pairs. Gabor coefficients are computed at automatically detected landmark locations and combined with powerful anthropometric features defined in the form of geodesic and Euclidean distances between pairs of fiducial points. We make the pragmatic assumption that the 2-D and 3-D data is acquired passively (e.g., via stereo ranging) with perfect registration between the portrait data and the range data. Statistical learning approaches are evaluated independently to reduce the dimensionality of the 2-D and 3-D Gabor coefficients and the anthropometric distances. Three parallel face recognizers that result from applying the best performing statistical learning schemes are fused at the match score-level to construct a unified multimodal (2-D+3-D) face recognition system with boosted performance. Performance of the proposed algorithm is evaluated on a large public database of range and portrait image pairs and found to perform quite well.
机译：我们介绍了一种新颖的多模式框架，用于根据从距离和肖像对图像中计算出的局部属性进行人脸识别。 Gabor系数是在自动检测到的地标位置处计算的，并与以基准点对之间的测地距离和欧几里得距离的形式定义的强大人体测量特征相结合。我们作出务实的假设，即在纵向数据和范围数据之间进行完美配准的情况下，被动地（例如，通过立体声测距）获取了2D和3D数据。对统计学习方法进行独立评估，以减少2-D和3-D Gabor系数的维数以及人体测量距离。将应用最佳性能的统计学习方案产生的三个并行人脸识别器在匹配分数级别进行融合，以构建具有增强性能的统一多模式（2-D + 3-D）人脸识别系统。该算法的性能在范围和肖像对的大型公共数据库中进行了评估，并且表现良好。

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