scholarly journals Facial Spots Detection Using Convolution Neural Network

2020 ◽  
pp. 71-83
Author(s):  
Payal Bose ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition System ◽  
The Body ◽  
Convolution Neural Network ◽  
Biometric Authentication ◽  
Human Face ◽  
Security Issue ◽  
Global Issue ◽  
The Face

Nowadays security became a major global issue. To manage the security issue and its risk, different kinds of biometric authentication are available. Face recognition is one of the most significant processes in this system. Since the face is the most important part of the body so the face recognition system is the most important in the biometric authentication. Sometimes a human face affected due to different kinds of skin problems, such as mole, scars, freckles, etc. Sometimes some parts of the face are missing due to some injuries. In this paper, the main aim is to detect a facial spots present in the face. The total work divided into three parts first, face and facial components are detected. The validation of checking facial parts is detected using the Convolution Neural Network (CNN). The second part is to find out the spot on the face based on Normalized Cross-Correlation and the third part is to check the kind of spot based on CNN. This process can detect a face under different lighting conditions very efficiently. In cosmetology, this work helps to detect the spots on the human face and its type which is very helpful in different surgical processes on the face.

scholarly journals Surgical face masks impair human face matching performance for familiar and unfamiliar faces

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Daniel J. Carragher ◽  
Peter J. B. Hancock
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Human Face ◽  
Face Matching ◽  
Face Recognition System ◽  
Surgical Masks ◽  
The Face ◽  
Matching Performance ◽  
Recognition Systems ◽  
Unfamiliar Faces

AbstractIn response to the COVID-19 pandemic, many governments around the world now recommend, or require, that their citizens cover the lower half of their face in public. Consequently, many people now wear surgical face masks in public. We investigated whether surgical face masks affected the performance of human observers, and a state-of-the-art face recognition system, on tasks of perceptual face matching. Participants judged whether two simultaneously presented face photographs showed the same person or two different people. We superimposed images of surgical masks over the faces, creating three different mask conditions: control (no masks), mixed (one face wearing a mask), and masked (both faces wearing masks). We found that surgical face masks have a large detrimental effect on human face matching performance, and that the degree of impairment is the same regardless of whether one or both faces in each pair are masked. Surprisingly, this impairment is similar in size for both familiar and unfamiliar faces. When matching masked faces, human observers are biased to reject unfamiliar faces as “mismatches” and to accept familiar faces as “matches”. Finally, the face recognition system showed very high classification accuracy for control and masked stimuli, even though it had not been trained to recognise masked faces. However, accuracy fell markedly when one face was masked and the other was not. Our findings demonstrate that surgical face masks impair the ability of humans, and naïve face recognition systems, to perform perceptual face matching tasks. Identification decisions for masked faces should be treated with caution.

scholarly journals Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 465
Author(s):  
Syeda Amna Rizwan ◽  
Ahmad Jalal ◽  
Munkhjargal Gochoo ◽  
Kibum Kim
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolution Neural Network ◽  
Contour Method ◽  
Connected Components ◽  
Active Shape Model ◽  
Human Face ◽  
Shape Model ◽  
Active Shape ◽  
The Face

The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification.

scholarly journals Face Recognition Using the Convolutional Neural Network for Barrier Gate System

2021 ◽  
Vol 15 (10) ◽  
pp. 138
Author(s):  
Mochammad Langgeng Prasetyo ◽  
Achmad Teguh Wibowo ◽  
Mujib Ridwan ◽  
Mohammad Khusnu Milad ◽  
Sirajul Arifin ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Recognition System ◽  
Average Error ◽  
The Real ◽  
The Face ◽  
Gate System ◽  
Recognition Technique

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.

scholarly journals Feature Based Face Recognition using Machine Learning Technniques

2020 ◽  
Vol 8 (6) ◽  
pp. 1313-1317
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Recognition ◽  
Euclidean Distance ◽  
Human Face ◽  
Cosine Transformation ◽  
The Face ◽  
Feature Based ◽  
Haar Cascade ◽  
Distinct Features

Human Face has Numerous unique Features to Distinguish between each other. Face can Identified by distinguishing between face and non-face followed by Identification. Traditionally face recognition uses distinct features Comparison to Identify the Faces which is Complex for larger databases and ambiguous in many scenarios. To improve the accuracy and Scalability Proposed method uses machine learning based Haar Cascade technique for face detection and convolutional neural network is used for feature extraction followed by classification using Euclidean distance and cosine transformation to recognize the face. The results demonstrate the work is performed well in recognizing the face efficiently with different variations.

scholarly journals Efficient Face Recognition System for Identifying Lost People

2021 ◽  
pp. 328-330
Author(s):  
Rutuja Parge ◽  
Nilam Patil ◽  
Pooja Yelve ◽  
Prof. Vidhya Gavali
Keyword(s):  
Neural Network ◽  
Social Issue ◽  
Face Recognition ◽  
Social Communication ◽  
Cell Phones ◽  
Recognition System ◽  
Significant Part ◽  
The Internet ◽  
Human Face ◽  
Face Recognition System

Lost Humans in our country and in different nations that they are known by everybody to be a significant social issue. These days distinguishing proof of a specific individual in the packed territory is a perplexing assignment. The human face assumes a significant part in our social communication, passing on individuals' personality. Face acknowledgment is an errand that people perform regularly and easily in their everyday lives. Face acknowledgment, as one of the essential biometric advances, turned out to be increasingly more significant inferable from fast advances in advances like computerized cameras, the Internet and cell phones, and expanded requests on security. For this, an answer is furnished on this with the assistance of a profound learning idea. Convolutional Neural Network (CNN) is utilized for the recognizable proof of an individual. The missing individual is distinguished utilizing different facial highlights. Face Detection assumes a significant part in this task. This framework tends to the structure of face acknowledgment framework by utilizing CNN technique. The CNN has been widely utilized for face acknowledgment calculations. It decreases the dimensionality of the picture, yet additionally holds a portion of the varieties in dataset of images.

scholarly journals A Deep Neural Network for face Recognition

2019 ◽  
Vol 8 (12S) ◽  
pp. 420-423
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Deep Neural Network ◽  
Facial Features ◽  
Biometric Authentication ◽  
Accuracy Rate ◽  
Learning Technique ◽  
The Face ◽  
Deep Learning Model

Face recognition is used to biometric authentication method to analyze the face extract and photographs useful to reputation formation from them, which can be usually called as a characteristic vector this is used to differentiate the organic features. In this paper to detect the suspect by extracting facial features from the captured image of the suspect from CCTV and match it with the pictures stored in the database and also to achieve an accuracy rate of 100 %, negligible loss using deep learning technique. For extracting the facial features, we are using deep learning model known as Convolutional Neural Network (CNN). It is one of the best models to extract features with the highest accuracy rate .

scholarly journals Attendance Posting Through Face Recognition

2019 ◽  
Vol 8 (4) ◽  
pp. 11652-11654
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Biometric Authentication ◽  
New Approach ◽  
Important Method ◽  
Security Issues ◽  
Detection Technology ◽  
Face Recognition System ◽  
The Face

Now a day’s face detection technology is widely used technique. It attracted attention for much valuable application in the market such as face recognition system. Biometric authentication is most important method in security system. Universally used Biometric fingerprint scanner can be bypassed quite easily. It can be broke easily. Biometric face recognition has been introduced to improve the security of a system. Methods such as Motion based and texture based are used for biometric face recognition. But these methods have less robustness and poor generalization ability. But apart from further security issues, this paper presents a new approach to make attendance of the student in class by the face recognition. Now a day’s attendance system is usually done manually or by the biometric fingerprint. Those are mistaken and tedious techniques. So this technique records the student’s participation in classroom consequently and provide facility for teachers for obtaining the data of the student effectively using log to check in and out time

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