Face Detection by Facial Features with Color Images and Face Recognition Using PCA

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

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Biometrical Processing of Faces in Security and Forensics

Handbook of Research on Computational Forensics, Digital Crime, and Investigation - Advances in Digital Crime, Forensics, and Cyber Terrorism ◽

10.4018/978-1-60566-836-9.ch004 ◽

2010 ◽

pp. 79-103

Author(s):

Pawel T. Puslecki

Keyword(s):

Face Recognition ◽

Face Detection ◽

Face Processing ◽

Facial Features ◽

Comprehensive Description ◽

Facial Processing ◽

Human Faces

The aim of this chapter is the overall and comprehensive description of the machine face processing issue and presentation of its usefulness in security and forensic applications. The chapter overviews the methods of face processing as the field deriving from various disciplines. After a brief introduction to the field, the conclusions concerning human processing of faces that have been drawn by the psychology researchers and neuroscientists are described. Then the most important tasks related to the computer facial processing are shown: face detection, face recognition and processing of facial features, and the main strategies as well as the methods applied in the related fields are presented. Finally, the applications of digital biometrical processing of human faces are presented.

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Face Detection and Gender Classification using Facial Features

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9131.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 6670-6674

Keyword(s):

Face Recognition ◽

Face Detection ◽

Recognition System ◽

Facial Features ◽

Gender Classification ◽

Biometric System ◽

Detection Techniques ◽

The Face ◽

And Gender ◽

Two Stages

Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier

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Challenges in Face Detection and Recognition techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h6307.069820 ◽

2020 ◽

Vol 9 (8) ◽

pp. 221-224

Keyword(s):

Face Recognition ◽

Face Detection ◽

Recognition System ◽

Research Paper ◽

Facial Features ◽

Image Information ◽

Public Crisis ◽

Face Recognition System ◽

Face Detection And Recognition ◽

Detection And Recognition

Face Recognition System is popular topic in the biometric world .This system provide Features to detect the person’s face and identify on basis of existing records in database .The aim of this study is to described how to show various facial features of an image. Face Recognition system, based on Biometric AI, uniquely finds out a person by analyzing the person's facial textures and shape. In this paper, our aim is to study various face detect and recognition techniques such as Harr Like Feature Algorithm resulting to retort criminality and public crisis. Also, some facial recognition approaches PCA and LDA have been discussed in the research paper for abstracting the image information.

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Face Detection With Face Mask for COVID-19 Pandemic Using Neural Networks

10.4018/978-1-7998-7709-7.ch012 ◽

2022 ◽

pp. 210-223

Author(s):

Nitish Devendra Warbhe ◽

Rutuja Rajendra Patil ◽

Tarun Rajesh Shrivastava ◽

Nutan V. Bansode

Keyword(s):

Neural Networks ◽

Face Recognition ◽

Face Detection ◽

Face Mask ◽

Detection Technique ◽

Facial Features ◽

Feature Points ◽

The People ◽

Biometric Systems ◽

The Face

The COVID-19 virus can be spread through contact and contaminated surfaces; therefore, typical biometric systems like password and fingerprint are unsafe. Face recognition solutions are safer without any need of touching any device. During the COVID-19 situation as all of the people are advised to wear masks on their faces, the existing face detection technique is not able to identify the person with face occlusion. The fraudsters and thieves take advantage of this scenario and misuse the face mask, favoring them to be able to steal and commit various crimes without being identified. Face recognition methods fail to detect or recognize the face as half of the face is masked and the features are suppressed. Face recognition requires the visibility of major facial features for face normalization, orientation correction, and recognition. Thus, the chapter focuses on the facial recognition based on the feature points surrounding the eye region rather than taking the whole face as a parameter.

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Implementasi Algoritma Hard Melokalisasi Fitur Wajah Pada Citra Warna

JSI: Jurnal Sistem Informasi (E-Journal) ◽

10.36706/jsi.v10i2.8055 ◽

2018 ◽

Vol 10 (2) ◽

Author(s):

Ujang Juhardi

Keyword(s):

Face Recognition ◽

Data Collection ◽

Face Detection ◽

Detection System ◽

Early Stage ◽

Recognition System ◽

Image Resolution ◽

Level Of Detail ◽

Facial Features ◽

The Face

AbstrakPendeteksian wajah (face detection) adalah salah satu tahap awal yang sangat penting dalam sistem pengenalan wajah (face recognition) yang digunakan dalam identifikasi biometrik. Sejauh ini, kendala utama yang dihadapi dalam sistem pendeteksian wajah berkisar pada masalah ukuran (resolusi citra), Resolusi citra merupakan tingkat detailnya suatu citra. Semakin tinggi resolusinya semakin tinggi pula tingkat detail dari citra tersebut. Haar like Feature merupakan metode yang lazim digunakan dalam pendeteksian obyek khususnya pendeteksian wajah dan fitur-fiturnya. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Untuk mengimplementasikan dan menganalisis kecepatan hasil algoritma haar dalam melokalisasikan fitur wajah penelitian ini menggunakan software MATLAB R2012b agar dapat mengetahui bagaimana cara menganalis dan mengimplementasikan serta mendapatkan hasil menganalisis pengaruh resolusi citra dari algoritma haar dalam melokalisasikan fitur wajah(mata,hidung, dan mulut). Penelitian ini dilaksanakan secara mandiri baik pengambilan data skunder maupun proses pengolahan datanya, untuk metode pengumpulan data pada penelitian ini penulis menggunakan metode studi pustaka dan studi laboratorium. Disarankan dengan adanya penelitian ini, penulis berharap dapat memberikan kontribusi kepada peneliti yang lain untuk meneliti pengaruh-pengaruh lain yang mempengaruhi keberhasilan algoritma haar, sehingga algoritma haar dapat dikembangkan lebih baik lagi.Kata kunci: Pendektesian wajah,resolusi citra, haar like featureAbstractFace detection is one of the early stage is very important in a facial recognition system (face recognition) used in biometric identification. So far, the main obstacle in the face detection system revolves around the issue size (image resolution), the image resolution is the level of detail of an image. The higher the resolution the higher the level of detail of that image .Haar like Feature is a method commonly used in the detection of objects particularly the face detection and its features. Principle Haar-like features are simple to recognize objects based on the value of the feature but not the pixel values of the image of that object. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. To implement and analyze speed haar algorithm results in a localized facial features of this research using MATLAB R2012b software in order to know how to analyze and implement and get the results to analyze the effect of image resolution algorithm localizes haar in the facial features (eyes, nose, and mouth). This research was carried out independently both secondary data collection and processing of data, for the data collection method in this study the authors use the method of literature and laboratory studies. Suggested the presence of this study, the authors hope to contribute to fellow-researcher to examine other influences which affect the success haar algorithms, so the algorithm can be developed haar better.Keywords: face detection, image resolution, haar like feature

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Facial Composite System Using Real Facial Features

Research Papers Faculty of Materials Science and Technology Slovak University of Technology ◽

10.2478/rput-2014-0029 ◽

2014 ◽

Vol 22 (35) ◽

pp. 9-15

Author(s):

Soňa Duchovičová ◽

Barbora Zahradníková ◽

Peter Schreiber

Keyword(s):

Face Recognition ◽

Face Detection ◽

Composite System ◽

Facial Feature ◽

Facial Features ◽

Feature Points ◽

Facial Image ◽

Facial Expression Classification ◽

Expression Classification ◽

Facial Composite

Abstract Facial feature points identification plays an important role in many facial image applications, like face detection, face recognition, facial expression classification, etc. This paper describes the early stages of the research in the field of evolving a facial composite, primarily the main steps of face detection and facial features extraction. Technological issues are identified and possible strategies to solve some of the problems are proposed.

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FDREnet: Face Detection and Recognition Pipeline

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2492 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3933-3938 ◽

Cited By ~ 6

Author(s):

D. Virmani ◽

P. Girdhar ◽

P. Jain ◽

P. Bamdev

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Face Detection ◽

Accurate Solution ◽

Facial Features ◽

Histogram Of Oriented Gradients ◽

Face Detection And Recognition ◽

Detection And Recognition

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 (EmbedNet). The approach allows the EmbedNet to learn how to distinguish facial features apart from recognizing them. This flexibility in learning due to contrastive loss accounts for better accuracy than using traditional deep learning losses. The dataset’s embeddings produced from the trained FDREnet result accuracy of 98.03%, 99.57% and 99.39% for face94, face95, and face96 datasets respectively through SVM clustering. Accuracy of 97.83%, 99.57%, and 99.39% was observed for face94, face95, and face96 datasets respectively through KNN clustering.

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