A Tilt-Angle Face Dataset And Its Validation

Wang, Nanxi; Wang, Zhongyuan; Zheng, He; Huang, Baojin; Zhou, Liguo; Han, Zhen

doi:10.1109/icip42928.2021.9506052

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…To overcome the limitations associated with face recognition by using video surveillance cameras, a dataset is presented in [33], wherein it is shown that, though deep learning models render impressive performance in facial recognition, they perform poorly in surveillance scenarios. It is further shown that the accuracy of face recognition depends not only on the structure of the model but also on the quality and diversity of the training samples.…”

Section: Single and Multiple Models For Face Recognition Systemsmentioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeling tag as a pattern name, including high face-matching accuracy and minimum speed of owing face-to-pose variations. Furthermore, the algorithm addresses the face rotation issue by introducing a mixture of error functions for execution time, accuracy loss, frame-wise failure, and identity loss, attempting to guide the authenticity of the produced image frame. Experimental results confirm effectiveness of the algorithm in terms of improved accuracy and reduced delay in the face-matching paradigm.

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Section: Single and Multiple Models For Face Recognition Systemsmentioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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“…WIDER FACE [15], Dark Face [30], MAFA [31], and TFD [32] are used to demonstrate the effectiveness and robustness of our method. WIDER FACE contains 32,203 images and 393,703 labeled faces with a high degree of variability in scale, pose, and occlusion.…”

Section: A Datasetsmentioning

confidence: 99%

Image Grid Recognition and Regression for Fast and Accurate Face Detection

Zhou

Chen

Zhang

et al. 2022

2022 26th International Conference on Pattern Recognition (ICPR)

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CNN-based face detection methods have achieved significant progress in recent years. However, for high performance face detection, there are still many challenging problems, e.g., the speed-accuracy balance and the performance degradation in adverse conditions. In this paper, by taking advantage of the characteristic of CNN, we propose an effective anchor generation and bounding-box regression method that can make a good balance between speed and accuracy, and also work well in bad conditions. The classic structure of CNN produces pyramid-like feature maps due to the pooling or other downscale operations. According to the size of a feature map, we divide the image into grids. Each grid corresponds to a point in the feature map. We make the corresponding feature point responsible for identifying the content of the grid. If this grid area belongs to the face area, it is a natural anchor for face bounding-box regression. Since this anchor is square, it is reasonable to use it to predict the face bounding-box which is square-like. The points in the lower-level feature map correspond to smaller grids, which are dedicated to predicting the bounding-boxes of smaller faces. The points in the higher-level feature maps correspond to larger grids, which are responsible for predicting the bounding-boxes of larger faces. Hence our method can effectively detect multi-scale faces. With this effectiveness, our method can achieve a high detection accuracy using fewer parameters which leads to a fast detection speed. The experiments demonstrate the effectiveness of our method.

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“…This paper conducts an in-depth study on this defect in most face datasets, aiming to construct a tilt-angle face sample in the top-down state to solve this problem and conduct experiments to verify the effectiveness of the constructed dataset. The main work of this paper includes: 1) We expand TFD [8] and collect TFD-B on the basis of TFD. We also collect face images from 6 top-down angles of each subject.…”

Section: Introductionmentioning

confidence: 99%

Face detection research based on a tilt-angle dataset

Cheng

Liu

Zhang

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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In the existing public face datasets, the horizontal frontal and left-right rotation poses are the majority, and the models trained by them can not meet the requirements of face detection in the overlooking situation. Aiming at this phenomenon, the Tilt-angle face dataset TFD is cited and further expanded, and the Tilt-angle face dataset TFD-B is manually collected. The RetinaFace algorithm is adopted to carry out multiple face detection experiments. Typical experiment A shows that compared with WiderFace, the average detection precision of TFD+TFD-B as training set is improved by 4.81% when looking down at 15°, 9.87% when looking down at 30°, 10.56% when looking down at 45°,12.63% when looking down at 60°, and 15.62% when looking down at 75°, which indicates that TFD+TFD-B can effectively improve the precision of face detection in the overlooking situation. At the same time, the experiments carried out further show that expanding the training dataset can improve the precision of face detection. TFD+TFD-B can be obtained at https://github.com/huang1204510135/DFD. Keywords-face detection; TFD; TFD-B; expand training dataset; detection precision Production of the Tilt-angle face dataset Comparative analysis of common face datasetsAlthough most public face datasets basically contain facial images in different poses, they show limited tilt angle variation in pitch direction due to the characteristics of random collection. In contrast, TFD+TFD-B contains 6 tilt angles in the pitch direction, which can provide enough overlooking information for the network. It is the most complete dataset of

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A Tilt-Angle Face Dataset And Its Validation

Cited by 5 publications

References 20 publications

Deep Learning Based Multi Pose Human Face Matching System

Deep Learning Based Multi Pose Human Face Matching System

Image Grid Recognition and Regression for Fast and Accurate Face Detection

Face detection research based on a tilt-angle dataset

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