Heterogenous output regression network for direct face alignment

Zhen, Xiantong; Yu, Mengyang; Xiao, Zehao; Zhang, Lei; Shao, Ling

doi:10.1016/j.patcog.2020.107311

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…As training should be performed for each cluster of landmarks, training cost increases. Authors in [61] used both non-linear layers and linear layers for face alignment. The non-linear layers with cosine activations encodes relationships between representations of images and shapes of facial landmarks.…”

Section: Related Workmentioning

confidence: 99%

An automated online proctoring system using attentive-net to assess student mischievous behavior

Potluri

Venkatramaphanikumar

2023

Multimed Tools Appl

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In recent years, the pandemic situation has forced the education system to shift from traditional teaching to online teaching or blended learning. The ability to monitor remote online examinations efficiently is a limiting factor to the scalability of this stage of online evaluation in the education system. Human Proctoring is the most used common approach by either asking learners to take a test in the examination centers or by monitoring visually asking learners to switch on their camera. However, these methods require huge labor, effort, infrastructure, and hardware. This paper presents an automated AI-based proctoring system- ‘Attentive system’ for online evaluation by capturing the live video of the examinee. Our Attentive system includes four components to estimate the malpractices such as face detection, multiple person detection, face spoofing, and head pose estimation. Attentive Net detects the faces and draws bounding boxes along with confidences. Attentive Net also checks the alignment of the face using the rotation matrix of Affine Transformation. The face net algorithm is combined with Attentive-Net to extract landmarks and facial features. The process for identifying spoofed faces is initiated only for aligned faces by using a shallow CNN Liveness net. The head pose of the examiner is estimated by using the SolvePnp equation, to check if he/she is seeking help from others. Crime Investigation and Prevention Lab (CIPL) datasets and customized datasets with various types of malpractices are used to evaluate our proposed system. Extensive Experimental results demonstrate that our method is more accurate, reliable and robust for proctoring system that can be practically implemented in real time environment as Automated proctoring System. An improved accuracy of 0.87 is reported by authors with the combination of Attentive Net, Liveness net and head pose estimation.

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Section: Related Workmentioning

confidence: 99%

An automated online proctoring system using attentive-net to assess student mischievous behavior

Potluri

Venkatramaphanikumar

2023

Multimed Tools Appl

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“…More specifically, it decomposes the original multi-target problem into several local sub-tasks, and combines their solutions. Also using deep neural networks, Zhen et al [22] proposed a general model that employs a non-linear layer and a linear low-rank layer to perform direct face alignment via multi-target regression. ERC [23] is an ensemble local approach where multiple regressors are chained in a random order.…”

Section: Multi-target Regressionmentioning

confidence: 99%

Deep tree-ensembles for multi-output prediction

Nakano

Pliakos

Vens

2022

Pattern Recognition

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“…In many practical applications, such as access control, we may only have very few or even one gallery image of each subject. In this case, it is hard to train or finetune a DNN using gallery images and a pre-trained face classification network is used for robust facial feature 30 5.60% 15.40% 7.52% CFAN 58 5.50% 16.78% 7.69% ESR 31 5.28% 17.00% 7.58% TCDCN 35 4.80% 8.60% 5.54% CFSS 59 4.73% 9.98% 5.76% LBF 56 4.95% 11.98% 6.32% DDFA 60 5.53% 9.56% 6.31% RAR 61 4.12% 8.35% 4.94% TR-DRN 62 4.10% 7.56% 4.99% DSRN 63 4.12% 9.68% 5.21% HORNet 64 4.68% 8.77% 5.68% Our method 3.90% 7.73%…”

Section: Daf Extractionmentioning

confidence: 99%

Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

Mao

Delei

Chen

et al. 2020

International Journal of Advanced Robotic Systems

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Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

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Heterogenous output regression network for direct face alignment

Cited by 11 publications

References 26 publications

An automated online proctoring system using attentive-net to assess student mischievous behavior

An automated online proctoring system using attentive-net to assess student mischievous behavior

Deep tree-ensembles for multi-output prediction

Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

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