2019
DOI: 10.48084/etasr.2492
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FDREnet: Face Detection and Recognition Pipeline

Abstract: Face detection and recognition are being studied extensively for their vast applications in security, biometrics, healthcare, and marketing. As a step towards presenting an almost accurate solution to the problem in hand, this paper proposes a face detection and face recognition pipeline - face detection and recognition embedNet (FDREnet). The proposed FDREnet involves face detection through histogram of oriented gradients and uses Siamese technique and contrastive loss to train a deep learning architecture (E… Show more

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Cited by 19 publications
(12 citation statements)
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“…On the other hand, the combination of aerial and underwater cameras to monitor the postures of FER was utilized in [23], whereas the CNN model achieved 99.78% accuracy [24]. An even more successful accuracy level was achieved in learning similarities and dissimilarities among the faces of dataset using FDREnet in [25].…”
Section: Literature Survey and Theoretical Frameworkmentioning
confidence: 99%
“…On the other hand, the combination of aerial and underwater cameras to monitor the postures of FER was utilized in [23], whereas the CNN model achieved 99.78% accuracy [24]. An even more successful accuracy level was achieved in learning similarities and dissimilarities among the faces of dataset using FDREnet in [25].…”
Section: Literature Survey and Theoretical Frameworkmentioning
confidence: 99%
“…Deep Learning techniques are a huge success in the field of computer vision. They have been deployed in many applications such as traffic sign detection and identification [1,2], indoor object detection and recognition [3][4][5] and many other applications [6,7]. The recognition of faces is a big challenge and an interesting research subject for different fields: psychology, model identification, computer vision, computer graphics, etc.…”
Section: Introductionmentioning
confidence: 99%
“…CNN-based methods have the capacity to learn complex features with deeper architectures and utilize training algorithms to learn informative object representations without the need to design the features manually [12]. Furthermore, researchers studied extensively various CNN models such as AlexNet [10], VGG [13], GoogLeNet [14], ResNet [15], and FDREnet [16] to improve the accuracy of classification and regression problems in machine learning. Generic object detection refers to the detection of objects from predefined classes obtaining the spatial location (e.g.…”
Section: Introductionmentioning
confidence: 99%