398,357 research outputs found
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Despite rapid advances in face recognition, there remains a clear gap between
the performance of still image-based face recognition and video-based face
recognition, due to the vast difference in visual quality between the domains
and the difficulty of curating diverse large-scale video datasets. This paper
addresses both of those challenges, through an image to video feature-level
domain adaptation approach, to learn discriminative video frame
representations. The framework utilizes large-scale unlabeled video data to
reduce the gap between different domains while transferring discriminative
knowledge from large-scale labeled still images. Given a face recognition
network that is pretrained in the image domain, the adaptation is achieved by
(i) distilling knowledge from the network to a video adaptation network through
feature matching, (ii) performing feature restoration through synthetic data
augmentation and (iii) learning a domain-invariant feature through a domain
adversarial discriminator. We further improve performance through a
discriminator-guided feature fusion that boosts high-quality frames while
eliminating those degraded by video domain-specific factors. Experiments on the
YouTube Faces and IJB-A datasets demonstrate that each module contributes to
our feature-level domain adaptation framework and substantially improves video
face recognition performance to achieve state-of-the-art accuracy. We
demonstrate qualitatively that the network learns to suppress diverse artifacts
in videos such as pose, illumination or occlusion without being explicitly
trained for them.Comment: accepted for publication at International Conference on Computer
Vision (ICCV) 201
Video-based driver identification using local appearance face recognition
In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms — Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1
Automatic face recognition of video sequences using self-eigenfaces
The objective of this work is to provide an efficient face recognition scheme useful for video indexing applications. In
particular we are addressing the following problem: given a set of known images and given a video sequence to be
indexed, find where the corresponding persons appear in the sequence. Conventional face detection schemes are not
well suited for this application and alternate and more efficient schemes have to be developed. In this paper we have
modified our original generic eigenface-based recognition scheme presented in [1] by introducing the concept of selfeigenfaces.
The resulting scheme is very efficient to find specific face images and to cope with the different face
conditions present in a video sequence. The main and final objective is to develop a tool to be used in the MPEG-7
standardization effort to help video indexing activities. Good results have been obtained using the video test sequences
used in the MPEG-7 evaluation group.Peer ReviewedPostprint (published version
Reference face graph for face recognition
Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation
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