FacePoseNet: Making a Case for Landmark-Free Face Alignment

Chang, Feng-Ju; Tran, Anh; Hassner, Tal; Masi, Iacopo; Nevatia, Ram; Medioni, Gérard

doi:10.1109/iccvw.2017.188

Cited by 125 publications

(73 citation statements)

References 54 publications

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“…The texture of the objects is also randomly changed. Finally, the 3D pose of the 3D generic model respect to the face image is estimated using [5] and then used to render the object and register its occlusion mask. Face Recognition Engine: We employ two state-of-the-art face recognition engines as a proxy to measure the impact of face completion to the final recognition accuracy.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Does Generative Face Completion Help Face Recognition?

Mathai¹,

Masi²,

AbdAlmageed³

2019

2019 International Conference on Biometrics (ICB)

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Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstructions or other objects covering parts of the face. While most of the current face recognition methods are not optimized to handle occlusions, there have been a few attempts to improve robustness directly in the training stage. Unlike those, we propose to study the effect of generative face completion on the recognition. We offer a face completion encoder-decoder, based on a convolutional operator with a gating mechanism, trained with an ample set of face occlusions. To systematically evaluate the impact of realistic occlusions on recognition, we propose to play the occlusion game: we render 3D objects onto different face parts, providing precious knowledge of what the impact is of effectively removing those occlusions. Extensive experiments on the Labeled Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that face completion is able to partially restore face perception in machine vision systems for improved recognition.

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Section: Experimental Evaluationmentioning

confidence: 99%

Does Generative Face Completion Help Face Recognition?

Mathai¹,

Masi²,

AbdAlmageed³

2019

2019 International Conference on Biometrics (ICB)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For 1:N face identification, we report the results by using TPIR vs. FPIR (equivalent to a DET curve) and Rank-N ( Table 5 and Figure 9 (b)). We compare three proposed models with VGGFace2 [2], Face-PoseNet (FPN) [3], Comparator Net [38], and PRN [14]. Similarity to evaluation on the IJB-A, all performance evaluations are based on the squared L 2 distance threshold.…”

Section: Ablation Studymentioning

confidence: 99%

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Kang

Kim

Jun

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9×9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing stateof-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.

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“…At present, there is no specifically designed neural networks for point cloud registration. FacePoseNet [4] directly regressed 6DoF transform parameters between the generic 3D facial keypoints model and the keypionts on intensity images. [14] proposed a network to estimate camera poses from monocular images in quaternion form.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition from Sequential Sparse 3D Data via Deep Registration

Yang

Lin

Xiao

et al. 2019

2019 International Conference on Biometrics (ICB)

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Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95 • and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.

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FacePoseNet: Making a Case for Landmark-Free Face Alignment

Cited by 125 publications

References 54 publications

Does Generative Face Completion Help Face Recognition?

Does Generative Face Completion Help Face Recognition?

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Face Recognition from Sequential Sparse 3D Data via Deep Registration

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