Face Recognition Scheme Research Articles

ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition

Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis.

Realtime face matching and gender prediction based on deep learning

<span lang="EN-US">Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.</span>

Facial Image Encryption for Secure Face Recognition System

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

Collaborative representation with hole filling techniques for Kinect RGB-D face recognition

We present in this paper, three different filtering techniques based on kernel function to fill missing information in depth map images obtained from low resolution sensor such as Kinect to improve the performance accuracy of RGB-D face recognition systems. We propose in this study, an RGB-D face recognition scheme that combines depth map and colour image using wavelet average fusion followed by collaborative representation classifier (CRC) for comparison of reference and probe images. We present the evaluation results based on our GU-RGBD face database and IIIT-D face database to present the significance of our three different filters employed in hole filling. Further, our investigation presents an extensive experimental analysis using eight different feature extraction techniques independently across three different filters to demonstrate the potential of our proposed approach. The proposed approach of hole filling, improves the performance accuracy of RGB-D face recognition as compared to without employing filtering operations.

Smart Door Lock Using Face Recognition

Most doors are controlled by persons using keys, security cards, passwords, or patterns to open the door. This paper aims to help users improve the door security of sensitive locations by using face detection and Recognition. The face is a complex multidimensional structure and needs good computing techniques for detection and Recognition. This paper comprises three subsystems: face detection, face Recognition and automatic door access control. Face detection is the process of detecting the region of the face in an image. The look is seen using the viola jones method, and face recognition is implemented using the Principal Component Analysis (PCA). Face Recognition based on PCA is generally referred to as the use of Eigenfaces. If a face is recognized, it is known, else it is unknown. The door will open automatically for the known person due to the command of the microcontroller. On the other hand, the alarm will ring for the unknown person. Since PCA reduces the dimensions of face images without losing essential features, facial images for many persons can be stored in the database. Although many training images are used, computational efficiency cannot be decreased significantly. Therefore, face recognition using PCA can be more beneficial for door security systems than other face recognition schemes.

Automatic Door Access System Using Face Recognition

Most doors are controlled by persons using keys, security cards, passwords, or patterns to open the door. This paper aims to help users improve the door security of sensitive locations by using face detection and Recognition. The face is a complex multidimensional structure and needs good computing techniques for detection and Recognition. This paper comprises three subsystems: face detection, face Recognition and automatic door access control. Face detection is the process of detecting the region of the face in an image. The look is seen using the viola jones method, and face recognition is implemented using the Principal Component Analysis (PCA). Face Recognition based on PCA is generally referred to as the use of Eigenfaces. If a face is recognized, it is known, else it is unknown. The door will open automatically for the known person due to the command of the microcontroller. On the other hand, the alarm will ring for the unknown person. Since PCA reduces the dimensions of face images without losing essential features, facial images for many persons can be stored in the database. Although many training images are used, computational efficiency cannot be decreased significantly. Therefore, face recognition using PCA can be more beneficial for door security systems than other face recognition schemes.

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

Privacy-Preserving Face Recognition in the Frequency Domain

Some applications may require performing face recognition (FR) on third-party servers, which could be accessed by attackers with malicious intents to compromise the privacy of users’ face information. This paper advocates a practical privacy-preserving FR scheme without key management realized in the frequency domain. The new scheme first collects the components of the same frequency from different blocks of a face image to form component channels. Only part of the channels are retained and fed into the analysis network that performs an interpretable privacy-accuracy trade-off analysis to identify channels important for face image visualization but not crucial for maintaining high FR accuracy. For this purpose, the loss function of the analysis network consists of the empirical FR error loss and a face visualization penalty term, and the network is trained in an end-to-end manner. We find that with the developed analysis network, more than 94% of the image energy can be dropped while the face recognition accuracy stays almost undegraded. In order to further protect the remaining frequency components, we propose a fast masking method. Effectiveness of the new scheme in removing the visual information of face images while maintaining their distinguishability is validated over several large face datasets. Results show that the proposed scheme achieves a recognition performance and inference time comparable to ArcFace operating on original face images directly.

Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption

Face recognition is playing an increasingly important role in present society, and suffers from the privacy leakage in plaintext. Therefore, a recognition system based on homomorphic encryption that supports privacy preservation is designed and implemented in this paper. This system uses the CKKS algorithm in the SEAL library, latest homomorphic encryption achievement, to encrypt the normalized face feature vectors, and uses the FaceNet neural network to learn on the image’s ciphertext to achieve face classification. Finally, face recognition in ciphertext is accomplished. After been tested, the whole process of extracting feature vectors and encrypting a face image takes only about 1.712s in the developed system. The average time to compare a group of images in ciphertext is about 2.06s, and a group of images can be effectively recognized within 30 degrees of face bias, with a recognition accuracy of 96.71%. Compared to the face recognition scheme based on the Advanced Encryption Standard encryption algorithm in ciphertext proposed by Wang et al. in 2019, our scheme improves the recognition accuracy by 4.21%. Compared to the image recognition scheme based on the Elliptical encryption algorithm in ciphertext proposed by Kumar S et al. in 2018, the total spent time in our system is decreased by 76.2%. Therefore, our scheme has better operational efficiency and practical value while ensuring the users’ personal privacy. Compared to the face recognition systems in plaintext presented in recent years, our scheme has almost the same level on recognition accuracy and time efficiency.

Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.

Clustering-based Efficient Privacy-preserving Face Recognition Scheme without Compromising Accuracy

Recently, biometric identification has been extensively used for border control. Some face recognition systems have been designed based on Internet of Things. But the rich personal information contained in face images can cause severe privacy breach and abuse issues during the process of identification if a biometric system has compromised by insiders or external security attacks. Encrypting the query face image is the state-of-the-art solution to protect an individual’s privacy but incurs huge computational cost and poses a big challenge on time-critical identification applications. However, due to their high computational complexity, existing methods fail to handle large-scale biometric repositories where a target face is searched. In this article, we propose an efficient privacy-preserving face recognition scheme based on clustering. Concretely, our approach innovatively matches an encrypted face query against clustered faces in the repository to save computational cost while guaranteeing identification accuracy via a novel multi-matching scheme. To the best of our knowledge, our scheme is the first to reduce the computational complexity from O(M) in existing methods to approximate O (√ M ), where M is the size of a face repository. Extensive experiments on real-world datasets have shown the effectiveness and efficiency of our scheme.

Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion

Cancellable biometrics is the art of generating distorted or encrypted templates of original biometric templates. The evolution of cancellable biometrics is attributed to the advanced hacking technologies that can capture the original stored biometrics from databases. One of the solutions for this problem is to store cancellable biometric templates in the database rather than the original ones. This paper presents a cancellable face recognition scheme that is based on face image encryption with Fractional-Order (FO) Lorenz chaotic system. The basic idea is to generate user-specific random keys to be XORed with the red, green, and blue components of color face images. These keys are generated from the fractional-order Lorenz chaotic system. In addition, some post-processing is implemented on the encrypted color components of the face images with rotation and transposition of matrices. Finally, a wavelet fusion process is applied on these encrypted and processed face image components. The reason behind the utilization of wavelet fusion is to generate a single cancellable template for each color face image. Furthermore, wavelet fusion after post-processing of encrypted color components leads to a better degree of diffusion in the encrypted face templates. Actually, the encryption with the proposed algorithm is not full encryption, but it is appropriate for cancellable biometric applications. Moreover, the proposed scheme is secure due to the power of fractional-order Lorenz chaotic system that is very sensitive to initial conditions selected by the user. In addition, the post-processing incorporated with wavelet fusion is a non-invertible process. The validation of the proposed scheme is performed with experiments on FERET, LFW, and ORL databases. Evaluation metrics including Equal Error Rate (EER), Area under the Receiver Operating Characteristic (AROC) curve are utilized in the proposed scheme. Numerical values reveal EER levels close to zero and AROC values of 100% at low and mild noise levels.

A Salient Binary Coding Scheme for Face and Expression Recognition from Facial Images

A Salient Binary Coding Scheme for Face and Expression Recognition from Facial Images

Implementation Face Recognition Attendance Monitoring System for Lab Surveillance with Hash Encryption

Face recognition (FR) is becoming popular to identify people. In fact, using the FR scheme, surveillance tasks can be built by recognizing people from their faces. This paper presents the implementation of face recognition as a biometric method for smart attendance as well as we also proposed the integrated scheme from capturing data from edge devices (CCTVs), streaming data to the dedicated server, then presenting the real-time data through android mobile devices. In this scheme, we proposed to employ deep learning algorithms based on the Convolutional Neural Network (CNN). Through the CCTV data streaming, faces are captured and matched with the database. Therefore, it is considered as their logging attendance. Furthermore, it is marked and stored into the database. This system prototype is developed by big data technology to tackle this complexity of data. The recognized faces can be monitored in real time monitoring. Eventually, real time reports are delivered through the web and android device with API after the data transmission is secured with hash encryption.

Face recognition with a new local descriptor based on strings of successive values

In this paper, a novel face recognition approach based on strings of successive values (SSV) is presented. In contrast to most of the existing local descriptors which encode only a limited number of pixels included in a mask, the strings extract more discriminative information over the whole face region, by moving from the current pixel to the next one, and to the other next, and so on, according to the variations of their intensities. Therefore, the SSV can be stopped in any place of the face area, which allows us to encode more edge information and texture information than the existing methods. The proposed face recognition scheme requires several steps. Firstly, the images are divided into non-overlapping sub-regions from which the strings are extracted since each pixel produces two different strings. Thereafter, the dictionary of visual words is created to reduce the number of strings obtained from each patch of the image. Therefore, the face image is described only by visual words, because each string is replaced by its nearest dictionary word. As a result, the occurrence of visual words is computed in a histogram as a face descriptor. Finally, the recognition is performed by using the nearest neighbor classifier with the Hellinger distance. The effectiveness of the proposed approach is evaluated on three different databases, and the experimental results show that the recognition performances achieved are competitive or even outperform the literature state of the art methods.

Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

To circumvent problems associated with dependence on traditional security systems on passwords, Personal Identification Numbers (PINs) and tokens, modern security systems adopt biometric traits that are inimitable to each individual for identification and verification. This study presents two different frameworks for secure person identification using cancellable face recognition (CFR) schemes. Exploiting its ability to guarantee irrevocability and rich diversity, both frameworks utilise Random Projection (RP) to encrypt the biometric traits. In the first framework, a hybrid structure combining Intuitionistic Fuzzy Logic (IFL) with RP is used to accomplish full distortion and encryption of the original biometric traits to be saved in the database, which helps to prevent unauthorised access of the biometric data. The framework involves transformation of spatial-domain greyscale pixel information to a fuzzy domain where the original biometric images are disfigured and further distorted via random projections that generate the final cancellable traits. In the second framework, cancellable biometric traits are similarly generated via homomorphic transforms that use random projections to encrypt the reflectance components of the biometric traits. Here, the use of reflectance properties is motivated by its ability to retain most image details, while the guarantee of the non-invertibility of the cancellable biometric traits supports the rationale behind our utilisation of another RP stage in both frameworks, since independent outcomes of both the IFL stage and the reflectance component of the homomorphic transform are not enough to recover the original biometric trait. Our CFR schemes are validated on different datasets that exhibit properties expected in actual application settings such as varying backgrounds, lightings, and motion. Outcomes in terms standard metrics, including structural similarity index metric (SSIM) and area under the receiver operating characteristic curve (AROC), suggest the efficacy of our proposed schemes across many applications that require person identification and verification.

Investigating of nodes and personal authentications utilizing multimodal biometrics for medical application of WBANs security

The authentication of the Wireless Body Area Networks (WBANs) nodes is a vital factor in its medical applications. This paper, investigates methods of authentication over these networks. Also, an effective unimodal and multimodal biometrics identification approaches based on individual face and voice recognition or combined using different fusion types are presented. The cryptography and non-cryptography-based authentication are discussed in this research work and its suitability with the medical applications. Cryptographic based authentication is not suitable for WBANs. The biometrics authentication is discussed and its challenges. In this work, different fusion types in multimodal biometric are presented. There are two unimodal schemes have been presented based on using the voice and face image individually, these two biometrics have been used in the multimodal biometric scheme. The presneted multimodal scheme is evaluated and applied using the feature and score fusion. The mechanism operation of presented algorithm starts with capturing the biometics signals ‘Face/Voice’, the second step is the feature extracting from each biometric individually. The Artificial Neural Network (ANN), The Support Vector Machine (SVM) and the Gaussian Mixture Model (GMM) classifiers have been employed to perform the classification process individually. The computer simulation experiments reveal that the cepstral coefficients and statistical coefficients for voice recognition performed better for the voice scenario. Also, the Eigenface and support vector machine tools in the face recognition scheme performed better than other schemes. The multimodal results better than the unimodal schemes. Also, the results of the scores fusion-based multimodal biometric scheme is better than the feature fusion-based scheme. Hence, the biometric-based authentication is effective and applicable for the WBANs authentication and personality continuous authentication on these medical applications wireless networks.

EyesGAN: Synthesize human face from human eyes

Face recognition recently has achieved remarkable success in many fields, especially in mobile payment, authentication, criminal investigation, and city management. However, face occlusion is still the key problem in person identification, such as in the field of anti-terrorism, criminal cases and public security. To solve this problem, an improved end-to-end deep generative adversarial network (named EyesGAN) has been proposed to synthesize human face from human eyes in this paper, which can be used as a potential scheme for masked face recognition. BicycleGAN is chosen as the baseline and effective improvements have been achieved. First, the self-attentional mechanism is introduced so that the improved model can more effectively learn about the internal mapping between human eyes and face. Second, the perceptual loss is applied to guide the model cyclic training and help with updating the network parameters so that the synthesized face can be of higher-similarity to the ground truth face. Third, EyesGAN has been designed by getting the utmost out of the performance of the perceptual loss and the self-attentional mechanism in GANs. A dataset of eyes-to-face synthesis has been reconstructed based on the public face datasets for training and testing. Finally, the faces synthesized by EyesGAN have been quantitatively and qualitatively compared with the results of existing methods. Extensive experiments demenstrate that our proposed method has performed better than the state-of-the-art methods in terms of Average Euclidean Distance, Average Cosine Similarity, Synthesis Accuracy Percentage, Fréchet Inception Distance, and Baidu face recognition rate (the accuracy achieved 96.1% on 615 test data of CelebA database). In this paper, the feasibility of synthesizing human face from human eyes has been explored, and the attention map shows that our network can predict other parts of the face from eyes.

Energy-efficient face detection and recognition scheme for wireless visual sensor networks

Energy-efficient and robust face detection and recognition scheme can be useful for many application fields such as security and surveillance in multimedia and visual sensor network (VSN). VSN consists of wireless resources-constrained nodes that are equipped with low-energy CMOS cameras for monitoring. On the one hand, captured images are meaningful multimedia-data that impose high energy consumption to be processed and transmitted. On the other hand, visual sensor (VS) is a battery-powered node with limited life-time. This situation leads to a trade-off between detection-accuracy and power-consumption. This trade-off is considered as the most major challenge for applications using multimedia data in wireless environments such as VSN. For optimizing this trade-off, a novel face detection and recognition scheme has been proposed in this paper based on VSN. In this scheme, detection phase is performed at VS and recognition phase is accomplished at the base station (sink). The contributions of this paper are in three folds: 1. Fast and energy-aware face-detection algorithm is proposed based on omitting non-human blobs and feature-based face detection in the considered human-blobs. 2. A novel energy-aware and secure algorithm for extracting light-weight discriminative vector of detected face-sequence to be sent to sink with low transmission-cost and high security level. 3. An efficient face recognition algorithm has been performed on the received vectors at the sink. The performance of our proposed scheme has been evaluated in terms of energy-consumption, detection and recognition accuracy. Experimental results, performed on standard datasets (FERET, Yale and CDnet) and on personal datasets, demonstrate the superiority of our scheme over the recent state-of-the-art methods.

Quaternion Based On Cancelable Biometrics

The current paper presents a novel cancelable face recognition scheme based on quaternion mathematics. The main idea of the proposed model is to mask the nature of faces prior to use in the face recognition process. The objective of this process is to keep the privacy of users during the initiation biometric data and the biometric verification process in case of data base compromising wherein it is possible to change the saved biometric templates while the color face image is used to compose a quaternion. Subsequently, a mask image is used in another quaternion. Quaternion multiplication and thus quaternion inverse are applied to generate the cancelable templates. The performance analysis of the proposed approach reveals that low EER and large area under ROC curve which are the required high index templates