Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10 . We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.
Face recognition is one of the most researched topics in Computer Vision and many detailed surveys exist @cite_11 @cite_51 @cite_4 @cite_44 . Here, we present the most relevant works to this paper and divide them into conventional methods which use hand crafted local and global features, deep learning based methods which are mainly based on various CNN architectures and data augmentation methods which focus on the problem of limited training data for learning.
@cite_44 @cite_51 @cite_4 @cite_11
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This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology. Face recognition is being widely accepted as a biometric technique because of its non-intrusive nature. Despite extensive research on 2-D face recognition, it suffers from poor recognition rate due to pose, illumination, expression, ageing, makeup variations and occlusions. In recent years, the research focus has shifted toward face recognition using 3-D facial surface and shape which represent more discriminating features by the virtue of increased dimensionality. This paper presents an extensive survey of recent 3-D face recognition techniques in terms of feature detection, classifiers as well as published algorithms that address expression and occlusion variation challenges followed by our critical comments on the published work. It also summarizes remarkable 3-D face databases and their features used for performance evaluation. Finally we suggest vital steps of a robust 3-D face recognition system based on the surveyed work and identify a few possible directions for research in this area. High performance for face recognition systems occurs in controlled environments and degrades with variations in illumination, facial expression, and pose. Efforts have been made to explore alternate face modalities such as infrared (IR) and 3-D for face recognition. Studies also demonstrate that fusion of multiple face modalities improve performance as compared with singlemodal face recognition. This paper categorizes these algorithms into singlemodal and multimodal face recognition and evaluates methods within each category via detailed descriptions of representative work and summarizations in tables. Advantages and disadvantages of each modality for face recognition are analyzed. In addition, face databases and system evaluations are also covered.