Unconstrained and constrained face recognition using dense local descriptor with ensemble framework

Kumar, Dipak; Garain, Jogendra; Kisku, Dakshina Ranjan; Sing, Jamuna Kanta; Gupta, Phalguni

doi:10.1016/j.neucom.2019.10.117

Cited by 25 publications

(7 citation statements)

References 30 publications

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“…Kumar et al [72] (2020) proposed an ensemble face recognition system that employed a novel descriptor named dense local graph structure (D-LGS). Besides, the descriptor employs a bilinear interpolation to improve the pixel density when generating the graphic picture from the entered image.…”

Section: Geometric Approachmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

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Section: Geometric Approachmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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“…Each sample is normalized with size 64 × 64. In this database, some samples were obtained under different conditions, such as illumination, facial expression, and facial detail [99][100][101][102]. Some samples from the ORL database are given in Figure.…”

Section: B the Orl Databasementioning

confidence: 99%

A Discriminative Vectorial Framework for Multi-modal Feature Representation

Gao

Guan

2021

Preprint

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“…In 2020, a novel local descriptor named Dense Local Graph Structure (D-LGS) was proposed by Kumar et al [45]. They used this descriptor for face recognition.…”

Section: Related Workmentioning

confidence: 99%

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

Hattab¹,

Behloul²

2018

ICST Transactions on Scalable Information Systems

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Face recognition is one of the most important topics in biometrics, where it achieved great success under controlled scenarios. Still, its accuracy degraded significantly in unconstrained conditions. To meet this challenge, we proposed a handcraft method based on extracting important regions from the face image. We have been using Scale-Invariant Feature Transform (SIFT) besides the Adaptive Local Ternary Patterns (ALTP). We have achieved an accuracy of 99.75% on the ORL database and 94.70% on the FERET database. Then, we proposed a second method based on deep learning to achieve more accurate face recognition. The deep learning models failed to achieve a high accuracy rate because they require a large amount of training data. We used firstly Data Augmentation to solve this failure. However, this solution does not show high performance. Secondly, our proposed ImageNet pre-trained AlexNet-v2 and VGG16 models with LinearSVC increased the accuracy rate to 100% for the both databases.

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Unconstrained and constrained face recognition using dense local descriptor with ensemble framework

Cited by 25 publications

References 30 publications

Past, Present, and Future of Face Recognition: A Review

Past, Present, and Future of Face Recognition: A Review

A Discriminative Vectorial Framework for Multi-modal Feature Representation

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

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