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The main challenge is to automate the model for aged or de-aged face generation. However, there are certain limitations on accuracy for age estimation and identity preservation. To achieve this, a new face age progression and regression is proposed by Hyper-parameter Tuning-Large Scale Generative Adversarial Network (HT-Large Scale GAN) with Pollination Rate-based Sunflower Dolphin Swarm Optimization (PR-SDSO). The input images are collected and fed into the object detection model, where the viola Jones algorithm is utilized. Here, the pre-processing is done by median filtering and contrast enhancement. The face age progression and regression are accomplished by novel HT-Large Scale GAN, where the hyperparameters are optimized by a new algorithm of PR-SDSO. Throughout the result analysis, the proposed model ensures that it provides the appropriate synthesized images for both the progression and regression phases and acquires less error to improve the quality of the image. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the Viola Jones and YOLOv3 algorithms for facial recognition under different conditions and highlights their strengths and weaknesses. Analysis focusses on facial emotions, angle recognition, lighting, and the effects of hidden facial features. YOLOv3 outperformed the Viola-Jones algorithm in angle-based recognition with more robustness. Both algorithms performed exceptionally well in different lighting conditions, with 100% recognition rates in artificial, natural, high-contrast, and dark surroundings. This shows that they are highly adaptive to changing lighting conditions. When individual facial characteristics, such as the forehead or eyes, were concealed, the Viola-Jones algorithm showed excellent reliability. When the nose and eyes were concealed, however, its performance dropped to 77%. YOLOv3, on the other hand, consistently achieved a 100% recognition rate, indicating that it handled inadequate facial data better, even in scenarios where multiple significant attributes were concealed. Both algorithms proven their resistance to dynamic face changes by achieving 100% recognition rates over a wide range of expressions and proving that facial expressions had no effect on their recognition accuracy. These algorithms should be improved in the future for extreme angles and partial occlusions, and their integration with other recognition methods should be investigated.

In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola–Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters’ weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques. [ABSTRACT FROM AUTHOR]

The enormous amount of data contained in the video image has grown rapidly along with surveillance, greatly outpacing the capacity of human resources to handle it effectively. Smart surveillance retrieval is an essential component of any modern video surveillance system, considerably boosting the effectiveness, precision, and interoperability of the system. The use of face recognition and other cutting-edge technology in the security surveillance system is rapidly rising. Therefore, in this article, the distributed deep convolutional neural network (DCNN) and distributed deep BiLSTM is proposed to efficiently detect the face from the video. One of the major contributions involved in this research relies on the key frame selection, where four unique distance measurement techniques are fused, and is named hybrid distance- key frame selection. The Tri birds optimization (TBO) technique selects the best solution from a large number of solutions for the ensemble model classifier engaged in face recognition. The ensemble model classifier incorporates various hyper-parameters that are optimally trained. Multiple test videos with 401 and 802 test videos are used as the input for the TBO-ensemble model that attains 97% accuracy, 98.33% precision, recall, and f-measure for epoch 50 and the 500 number of retrievals, respectively. [ABSTRACT FROM AUTHOR]

Detecting and analyzing human face represent a necessity in many areas such as medicine, security, education, human-computer interaction, and much more. Face emotions recognition (FER) is one of the important aspects of face image analysis that is needed in many applications and technologies. In general, the research work in this area concentrates on enhancing the detection accuracy and decreasing the time needed for detection. In this work, the FER hybrid algorithm based on V-J (Viola-Jones) and CNN algorithms is tested, analyzed, and evaluated considering different aspects among which are the principle of work and factors involved and affecting it, the efficiency, the prospect of improving, and the possibility of exchanging the role between the V-J and CNN algorithms. [ABSTRACT FROM AUTHOR]

In this digital world, technology mainly revolves around multimedia content, which is massive. The existing techniques are inadequate to capture the movement of the human upper body parts in real-time. Therefore, a system is urgently required to track the human upper body parts movements from image and video content. The Viola–Jones algorithm based on a novel technique is proposed to detect upper body parts like the eyes and face in this work. Moreover, a face tracking algorithm is employed to track the movement of the upper body parts in real-time. The proposed model improves 13%, 18%, and 10% accuracy for face, eye, and body on Grimace dataset, Face94 Dataset, and Milborrow University of Cape Town (MUCT) dataset, respectively. Furthermore, it is also tested directly on the MATLAB software on a new image or Cam video. It takes less than 25 milliseconds per frame; hence, applicable for real-time applications. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of O (P + m L N) , justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome. [ABSTRACT FROM AUTHOR]

Adaptive video monitoring settings have been extensively deployed in recent years. Smart video monitoring technology enables the acquisition and analysis of movies from various devices, as well as automatic analysis based on knowledge gathering. However, the storage capacity is restricted, and the important frames from the movie cannot be saved. Though, if the movie employs face as keyframes, it creates space and time complexity. To address this issue, the Viola-Jones Algorithm was used to detect faces from extracted keyframes in Video Indexing through Human Face Images using LGFA and the sliding window technique. The image gradient for brightness is created by integrating the sliding windowing method with LGFA, and scanning the input image horizontally takes up 70% of the facial image. As a result, Barcode as an index using the sequence table of the EAN 8 approach converts a video's human face into an EAN-8 linear video indexing barcode and thereby reducing bandwidth, storage space, and time complexity. Regular TV series video datasets, datasets of YouTube faces, and data sets of Hollywood clips were used to evaluate the proposed technique, and shown to be effective for indexing videos based on human faces. [ABSTRACT FROM AUTHOR]

Facial expression recognition in the world is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect for analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. This paper deals with Facial expression detection and recognition through the Viola-jones algorithm and HCNN using the LSTM method to avoid those challenges in recent work. For face detection, basically, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the HCNN and LSTM model for efficiently identifying the facial expression. In existing work, the feature extraction stage exact finding of the corner becomes a very difficult task, to solve this issue need to use a corner detection algorithm to enhance the feature extraction (corner points) from the face image. One of the main drawbacks of WOA is that it is not good at exploring the search space. To overcome those issues, the work introduced an improved framework for facial image recognition. In which first edges are detected using the canny edge detection operator. Then improved linear contrast stretching is used for image enhancement. Then in feature extraction, the author proposes Hybrid SIFT with Double d-LBP (Dd-LBP) to obtain the features that are illumination and pose independent. For face detection, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and significant features are selected by using self-learning chicken swarm optimization, it minimizes the feature vector is given into the Hybrid HCNN and LSTM model for efficiently identifying the facial expression. Experimental results demonstrate the efficiency of the proposed work in terms of accuracy, precision, recall and f-measure.

Real-time facial recognition systems have been increasingly used, making it relevant to address the accuracy of these systems given the credibility and trust they must offer. Therefore, this article seeks to identify the algorithms currently used by facial recognition systems through a Systematic Literature Review that considers recent scientific articles, published between 2018 and 2021. From the initial collection of ninety-three articles, a subset of thirteen was selected after applying the inclusion and exclusion procedures. One of the outstanding results of this research corresponds to the use of algorithms based on Artificial Neural Networks (ANN) considered in 21% of the solutions, highlighting the use of Convolutional Neural Network (CNN). Another relevant result is the identification of the use of the Viola-Jones algorithm, present in 19% of the solutions. In addition, from this research, two specific facial recognition solutions associated with access control were found considering the principles of the Internet of Things, one being applied to access control to environments and the other applied to smart cities.

This article introduces a new cancellable multi-biometric system based on the combination of a selective encryption method and a deep-learning-based fusion technology. The biometric face image is treated with an automatic face segmentation algorithm (Viola-Jones), and the image of the selected eye is XORed with a PRNG (Pseudo Random Number Generator) matrix. The output array is used to create a primary biometric template. This process changes the histogram of the selected eye image. Arnold's Cat Map is used to superimpose the PRN pixels only on the pixels of the primary image. Arnold's cat map deformed eyes are encrypted using the Advanced Encryption Standard (AES) to encrypt the biometric data stored in the database. In addition, the AES master key is used for the same person in the identity verification process to verify the biometric identity. It is created from the fingers of the right hand, and the right eye is integrated into this process using deep learning technology. The deep learning fusion process can prevent attacks on the biometric system as a whole. In order to avoid damage to the eye or fingerprint images, the design considers the other eye and fingerprint images. [ABSTRACT FROM AUTHOR]

Wearing a face mask is one of the effective barriers against the coronavirus COVID-19 pandemic. It offers protection according to the World Health Organization and many medical papers. This paper proposes a method for masked face recognition in order to force the population to put on masks and reduce the COVID-19 pandemic in the world. The Viola-Jones algorithm is used to detect the face, and the Histogram of Oriented Gradients (HOG) technique was used to extract the relevant features from face images. The performance of the proposed algorithm is analysed for different data using two common image classification methods, including support vector machines and K Nearest Neighbor (KNN) algorithm for machine learning, which are used to classify the feature vectors. Their performance was compared and evaluated using accuracy. In this case, the experimental result shows that the support vector machine classifier achieved the highest accuracy and surpasses the KNN method in mask detection with an accuracy of 99.43%. [ABSTRACT FROM AUTHOR]

Real-time facial recognition systems have been increasingly used, making it relevant to address the accuracy of these systems given the credibility and trust they must offer. Therefore, this article seeks to identify the algorithms currently used by facial recognition systems through a Systematic Literature Review that considers recent scientific articles, published between 2018 and 2021. From the initial collection of ninety-three articles, a subset of thirteen was selected after applying the inclusion and exclusion procedures. One of the outstanding results of this research corresponds to the use of algorithms based on Artificial Neural Networks (ANN) considered in 21% of the solutions, highlighting the use of Convolutional Neural Network (CNN). Another relevant result is the identification of the use of the Viola-Jones algorithm, present in 19% of the solutions. In addition, from this research, two specific facial recognition solutions associated with access control were found considering the principles of the Internet of Things, one being applied to access control to environments and the other applied to smart cities. [ABSTRACT FROM AUTHOR]

Hand gesture detection and recognition is a cutting-edge technology that is getting progressively applicable in several applications, including the recent trends namely Virtual Reality and Augmented Reality. It is a key part of Human-Computer Interaction which gives an approach to two-way interaction between the computer and the user. Currently, this technology is limited to expensive and highly specialized equipment and gadgets such as Kinect and the Oculus Rift. In this paper, various technologies and methodologies of implementing a gesture detection and recognition system are discussed. The paper also includes the implementation of a face recognition module using the Viola-Jones Algorithm for authentication of the system followed by hand gesture recognition using CNN to perform basic operations on the laptop. Any type of user can use gesture control as an alternative and interesting way to control their laptop. Furthermore, this can be used as a prototype for future implementations in the field of virtual reality as well as augmented reality.

Nowadays, the usage of a manual attendance system has become irrelevant with the current technological advancements. Although, there are many advanced attendance systems such as RFID attendance. It does not solve the problem face in the manual system, like inaccurate data, falsification of attendance, and time-consuming. Therefore, this paper suggests a face recognition attendance system to improve these drawbacks, as it can offer accurate attendance and required less time in recording attendance. Viola-Jones algorithm and Histogram of Oriented Gradient (HOG) feature extraction are techniques applied in this paper. The Viola-Jones algorithm will detect faces on the received image while the HOG feature extraction technique algorithm extracts the facial feature and compare it with the database for face recognition. An android application was developed for the user to upload and check the attendance record done by the face recognition process. [ABSTRACT FROM AUTHOR]

Facial expression recognition in the world is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect for analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. This paper deals with Facial expression detection and recognition through the Viola-jones algorithm and HCNN using the LSTM method to avoid those challenges in recent work. For face detection, basically, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the HCNN and LSTM model for efficiently identifying the facial expression. In existing work, the feature extraction stage exact finding of the corner becomes a very difficult task, to solve this issue need to use a corner detection algorithm to enhance the feature extraction (corner points) from the face image. One of the main drawbacks of WOA is that it is not good at exploring the search space. To overcome those issues, the work introduced an improved framework for facial image recognition. In which first edges are detected using the canny edge detection operator. Then improved linear contrast stretching is used for image enhancement. Then in feature extraction, the author proposes Hybrid SIFT with Double δ-LBP (Dδ-LBP) to obtain the features that are illumination and pose independent. For face detection, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and significant features are selected by using self-learning chicken swarm optimization, it minimizes the feature vector is given into the Hybrid HCNN and LSTM model for efficiently identifying the facial expression. Experimental results demonstrate the efficiency of the proposed work in terms of accuracy, precision, recall and f-measure. [ABSTRACT FROM AUTHOR]