Chapter 10 - Local plastic surgery-based face recognition using convolutional neural networks

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Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

Facial Plastic Surgery & Aesthetic MedicineVol. 24, No. 4 Surgical PearlStep-by-Step Let Down Preservation Rhinoplasty TechniqueJeanie Sozansky Lujan and Jose Enrique BarreraJeanie Sozansky Lujan*Address correspondence to: Jeanie Sozansky Lujan, MD, Austin Face and Body, 7004 Bee Caves Rd, Building 2, Ste 100, TX 78746, USA. E-mail Address: sozansjm@gmail.comhttps://orcid.org/0000-0003-3387-2676Texas Center for Facial Plastic and Laser Surgery, San Antonio, Texas, USA.Search for more papers by this author and Jose Enrique BarreraTexas Center for Facial Plastic and Laser Surgery, San Antonio, Texas, USA.Search for more papers by this authorPublished Online:29 Jul 2022https://doi.org/10.1089/fpsam.2020.0612AboutSectionsView articleView Full TextSupplemental MaterialPDF/EPUBView Supplemental Data Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Step-by-Step Let Down Preservation Rhinoplasty Technique." Facial Plastic Surgery & Aesthetic Medicine, 24(4), pp. 314–315FiguresReferencesRelatedDetailsCited byFunctional and Aesthetic Outcomes of Let Down Dorsal Preservation Rhinoplasty Jeanie Sozansky Lujan, Jared M. Goldfarb, and Jose Enrique Barrera3 March 2023 | Facial Plastic Surgery & Aesthetic Medicine, Vol. 25, No. 2Application of Ultrasonic Piezoelectric Technology for Rhinoplasty José Enrique Barrera15 June 2022 | Facial Plastic Surgery & Aesthetic Medicine, Vol. 24, No. 3 Volume 24Issue 4Aug 2022 Information© 2022, American Academy of Facial Plastic and Reconstructive Surgery, Inc.To cite this article:Jeanie Sozansky Lujan and Jose Enrique Barrera.Step-by-Step Let Down Preservation Rhinoplasty Technique.Facial Plastic Surgery & Aesthetic Medicine.Aug 2022.314-315.http://doi.org/10.1089/fpsam.2020.0612Published in Volume: 24 Issue 4: July 29, 2022Online Ahead of Print:July 15, 2021PDF download

In this paper, face recognition algorithms under three low-quality conditions, namely, complex illumination, low resolution and partial occlusion, are summarized, and relatively new face recognition algorithms under low-quality conditions are introduced and explained. Such as the algorithm of face recognition with occlusion based on partition, face recognition algorithm based on Iteratively Reweighted Robust Principal Component and face recognition with occlusion based on MEBML, illumination face recognition based on RPCA and Convolutional Neural Network, face recognition based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern, low-resolution face recognition based on blocking CS-LBP and weighted PCA algorithm, pose Robust low-resolution face recognition via CKEDA and other face recognition algorithm under low-quality conditions. The existing problems and development trend of face recognition under low quality conditions are analyzed.

In recent years, technologies related to face recognition have achieved rapid development. Face recognition Algorithms using Convolutional Neural Network (CNN) in the field of deep learning have been improved compared with traditional ones in terms of recognition accuracy, anti-interference ability and the speediness of identification. The mature face recognition technology now plays a decisive role in many areas, and also one of the key technologies in the facilitation of unmanned supermarkets. However, most algorithms nowadays just use the face database in unrestricted scenes to train, verify and evaluate. Their performance in practical applications is not satisfactory enough. In this paper, we eager to find a face recognition algorithm and system that are more suitable for unmanned supermarket scenes. After in-depth analysis of the scene, we find out that motion blur and large-scale recognition are two important reasons why current algorithms do not perform well. Aim to solve these problems, we summarize and propose new algorithms based on the existing ones. The experimental results show that the algorithm has effective face recognition ability in unmanned supermarket.

The impacts of plastic surgery on face recognition systems have been investigated in the past decade by many researchers. Diverse well-known face recognition approaches, e.g. based on PCA or LBP, have been bench-marked mostly on the web-collected IIITD plastic surgery face database. Generally, significant performance drops were reported when comparing facial images taken before and after plastic surgeries. On the one side, some researchers reported problems with said plastic surgery database, i.e. the presence of low quality images. On the other side, the applied methods no longer reflect the state-of-the-art in face recognition. This calls for evaluating the impact of plastic surgery on state-of-the-art deep face recognition systems anew considering high quality imagery of most relevant plastic surgeries.This work introduces the new Hochschule Darmstadt (HDA) plastic surgery database of facial images taken before and after surgery. This database vastly complies with the quality requirements defined by the International Civil Aviation Organization (ICAO) for electronic travel documents and comprises face images of the five most frequently applied facial plastic surgeries. The HDA plastic surgery database, the IIITD plastic surgery database, and a non-surgery database, i.e. ICAO-compliant subsets of the FRGCv2 and FERET datasets, are used for comparative verification and identification evaluations which are conducted using the commercial Cognitec FaceVACS system and the open-source ArcFace system. The obtained results suggest that the impact of plastic surgery on deep face recognition systems is less significant than that observed for previously benchmarked methods.

Facial Plastic and Reconstructive Surgery

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

Face recognition has been of interest to a growing number of researchers due to its applications on security. There are numerous face recognition algorithms proposed by researchers. However, there is no unified framework for the integration. In this paper, we implement different existing well-known algorithms, eigenface, Fisher-face, elastic graph matching (EGM), support vector machine (SVM) and neural network, to give a comprehensive testing under same face databases. Moreover, we present a face recognition committee machine (FRCM), which is a novel approach for assembling the outputs of various face recognition algorithms to obtain a unified decision with improved accuracy. The machine consists of an ensemble of the above algorithms to cope with various face images. We have tested our system with ORL face database and Yale face database. A comparative experimental result of different algorithms with the committee machine demonstrates that the proposed system achieves improved accuracy over the individual algorithms.

Face recognition is a common means of identity authentication. Mobile learning platform login technology has developed from user name and password to face recognition. In order to improve effectively the rate of face recognition, this paper proposes a kind of face recognition algorithm based on self-adaptive blocking local binary pattern (LBP) and dual channel convolutional neural network (CNN) with different convolution kernels. Firstly, the Gamma correction, the Mallet wavelet filtering and normalization are used to preprocess the face image. The face image is decomposed and reconstructed by 2-layer Mallet wavelet to filter out the interference signal effectively. Although the general LBP operator extracts the overall texture and contour features of the face image, the distribution of the bright spot, dark spot and other micro details cannot be fully characterized. In order to solve this problem, integral projection is introduced to project the image horizontally and vertically. The extreme points of the projection represent the texture mutation points of the face image in the horizontal and vertical directions. These extreme points are used as the boundary of the image blocking, and the LBP value of the face image is extracted by the self-adaptive blocking strategy. Combining the features of k-nearest neighbor classifier and softmax, a k-softmax classification method is proposed to classify and recognize the face image labels. After two channel network structure training, this method is tested on Yale, ORL, extended Yale B and self-built face databases by five experiments, comparing with other face recognition algorithms. The results show that the proposed method based on SAB-LBP and dual channel CNN has high recognition rate and computational efficiency.

In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.

Facial recognition is a feature that uses facial detection algorithms to detect a face and then invokes facial recognition algorithms to try to match the person’s face. First, a person’s face needs to be enrolled; during enrollment, the person’s facial details are saved in a database—in this case, a mobile phone. The facial recognition algorithm uses this database to match the currently presented face with the faces saved in the database. The efficiency of a facial recognition algorithm depends upon the speed at which it can detect and recognize faces. The problem we address in this paper is that a reliable, automated method for testing facial recognition features in mobile phones. Because the multimedia capabilities of smartphones have expanded phenomenally, the need for thoroughly testing facial recognition algorithms has become crucial. The uses of facial recognition can range from facial authentication for unlocking phones to security for a number of other applications. To meet these needs, a reliable, automated test for validating the facial recognition algorithms must be developed. The challenge for the software test team was to automate the test cases, which involve tilting the phone at specific angles from the test subject. The permissible angular movements of the phone are determined by the algorithmic specifications of the facial recognition algorithm. We tested scenarios involving multiple faces, motion blur, panning the phone in front of the test subject faces at various speeds, and so on. We adopted a robotic arm to perform the facial recognition test cases and developed software to program the robotic arm to test the phone’s facial recognition software for functionality, performance, and stability as well as adversarial tests. This paper discusses the computer vision use case for facial recognition and describes how we developed an automated facial recognition test suite using a robotic arm.

In this paper, it studies two critical technical links of face detection and face recognition in the recognition of face, and conducts analyses and comparisons on some commonly used face detections and recognition algorithms. Through the collation of this information and the combination of related knowledge in the digital image processing, it proposes a face recognition algorithm based on the singular value decomposition. After preprocessing face images, by the use of the projection method, it obtains positions of five sense organs, and then extracts local feature values near five sense organs as the main features of faces by using the singular value decomposition. For different face images of the same individual, the matching degree of feature values will be very high.KeywordsFace recognitionimage recognitionlocal informationfeature extraction

Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However,these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.