Multi-View Face Detection and Landmark Localization Based on MTCNN

Ma, Ming; Wang, Jianji

doi:10.1109/cac.2018.8623535

Cited by 23 publications

(7 citation statements)

References 13 publications

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“…Similarly, in the last stage more powerful CNN is used and further improvement to the detected faces is done. where five facial landmarks are generated [83]. MTCNN which has been successfully used for face detection in ( [2], [84], [85], [86]), features an advantage over FaceNet is that it can simultaneously detect more than one face in an image and feed them to a recognition system.…”

Section: ) Mtcnnmentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

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Section: ) Mtcnnmentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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“…Without having pictures of variant faces, work will not proceed. An MTCNN is used to identify and bring actual face parts from a given picture in a position to beat multifarious face recognition standards offering real-time performance with high precision studied [26]. In this system, a pretrained model (MTCNN) is used to find the candidate's face in part of the image and interpret it into greater feature facial descriptors [27].…”

Section: Face Detection and Reductionmentioning

confidence: 99%

“…where the probability of face sample x i is represented by p 1 which is predicted by the MTCNN. y det i stands for groundtruth; y det i ∈ {0, 1} [26].…”

Section: Face Judgementmentioning

confidence: 99%

Smart Identity Management System by Face Detection Using Multitasking Convolution Network

Farhi

Abbasi

Rehman

2021

Security and Communication Networks

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Identity management system in most academic and office environments is presently achieved primarily by a manual method where the user has to input their attendance into the system. The manual method sometimes results in human error and makes the process less efficient and time-consuming. The proposed system highlights the implementation and design of a smart face identification-based management system while taking into account both the background luminosity and distance. This system detects and recognizes the person and marks their attendance with the timestamp. In this methodology, the face is initially resized to 3 different sizes of 256, 384, and 512 pixels for multiscale testing. The overall outcome size descriptor is the overall mean for these characteristic vectors, and the deep convolution neural network calculates 22 facial features in 128 distinct embeddings in 22-deep network layers. The pose of the 2D face from −15 to +15° provides identification with 98% accuracy in low computation time. Another feature of the proposed system is that it is able to accurately perform identification with an accuracy of 99.92% from a distance of 5 m under optimal light conditions. The accuracy is also dependent on the light intensity where it varies from 96% to 99% under 100 to 1000 lumen/m2, respectively. The presented model not only improves accuracy and identity under realistic conditions but also reduces computation time.

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“…Precise recognition of landmarks is performed using Mediapipe [31], [32], [33], [34], [35] which is mainly used in real-time applications such as emotion detection, Parkinson's disease detection, driver drowsiness detection, and earlystage autism screening [36], [37], [38], [39], [40], [41]. It estimates 468 landmarks in real-time to improve the accuracy of the face recognition system (FRS) compared to other existing approaches, such as Multi-Task Cascaded Convolutional Networks (MTCNN) [42], [43] and Digital Library (DLIB) [44], [45]. After that, Euclidean and Geodesic distances were measured from the selected landmark points to extract the features.…”

Section: Introductionmentioning

confidence: 99%

2D-3D Facial Image Analysis for Identification of Facial Features Using Machine Learning Algorithms With Hyper-Parameter Optimization for Forensics Applications

et al. 2023

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Recognizing a face is a remarkable process that humans naturally use. Computer vision has tried to resemble this ability of human vision as a biometric tool to identify humans. Commercial and law enforcement applications are increasingly using face recognition technology to identify people. Currently, it is one of the most sought-after detection methods used in forensics for criminal identification purposes. Owing to similarities in the appearance of certain faces, especially in criminal cases, this problem poses a great challenge in forensic investigation and detection. The novelty of this work lies in the development of a framework for face recognition using 2D facial images gathered from various sources to generate a 3D face mesh using 468 MediaPipe landmarks which detects multiple faces in real-time. This leads to the generation of input feature vectors being formulated utilizing Euclidean/Geodesic distances and their ratios between the landmarks. These feature vectors are then trained into various classifiers that can provide the correct matching decision in an unrestricted environment such as large pose, expression, and occlusion variations. These quantitative similarity measures can then be presented as statistical evidence to identify criminals in forensic investigations. This two-dimensional to three-dimensional annotation provides a higher quality of threedimensional reconstructed face models without the need for any additional three-dimensional morphable models. The proposed methods were validated and tested to achieve comparable recognition performance using hyperparameter optimization. Regarding accuracy, the statistical results show that Extreme gradient boosting is the best classification model that provides the best accuracy (78%) for predicting facial images compared with Adaptive Boosting (77%), Random Forest (75%), Bernoulli Naive Bayes (68%), Decision Tree (57%), Logistic Regression (71%), Light Gradient Boosting Model (58%), Extra Tree Classifier (57%), Support Vector Machine (58%), and Nearest Centroid (62%) classifiers which can be further increased by considering a greater number of images in the dataset implying at the potential of further research for scale-up implementation.

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Multi-View Face Detection and Landmark Localization Based on MTCNN

Cited by 23 publications

References 13 publications

Hardware Accelerators for Real-Time Face Recognition: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

Smart Identity Management System by Face Detection Using Multitasking Convolution Network

2D-3D Facial Image Analysis for Identification of Facial Features Using Machine Learning Algorithms With Hyper-Parameter Optimization for Forensics Applications

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