LSCO: Light spectrum chimp optimization based spinalnet for live face detection and recognition

Face recognition and facial expression recognition using local patterns is the order of the day. Local directional number pattern (LDNP) is one of the prominent descriptor for face recognition. LDNP assigns a 3 bit code for each pixel in the image. The resultant LDNP labeled image is divided into regions to form histogram based descriptor. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDNP, a dimensionality reduced local directional number pattern (DR-LDNP) is proposed in this paper. The proposed descriptor computes single code for each block. This is done by X-ORing of the LDNP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDNP coding pattern constraints. The resultant DR-LDNP descriptor outperforms the existing methods. The experimentation is carried out on standard databases like FERET, YALE, ORL, Cohn–Kannade and JAFFEE and obtained good recognition rates compared to other methods.

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

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

In this paper, we present novel approaches of automatically detecting human faces in images which is extremely important for any face recognition system. This paper expands on the traditional Viola-Jones approach by proposing to boost a plethora of mixed feature sets for face detection; we do this by adding non-Haar-like elements to a large pool of mixed features in an Adaboost framework. We show how to generate discriminative support vector machine (SVM) type features and Gabor-type features (in various orientations and frequencies and central locations) and use this whole pool as possible discriminative candidate feature sets in modeling the patterns of a frontal view human face. This general and large-diversity pool of features is used to build a boosted strong classifier and we show we can improve the generalization performance of the AdaBoost approach, and as a result improving the robustness of the face detector. We report performance on the MIT+CMU face database and compare the result with other published face detection algorithms. We also discuss processing times and speeding up methods to offset the increase in complexity in order to achieve face detection in real time.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications including, but not limited to, human behaviour understanding, detection of mental disorders, and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in four-stages consisting of pre-processing, face detection, feature extraction, and expression classification. The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control

Gaining knowledge from health data may contribute to novel approaches or improved patient care plans. To intelligently acquire knowledge from the stroke data, machine learning (ML) technique is utilised and healthcare systems are used. Globally, security has become a major concern, and biometric technologies such as face detection and recognition technologies have been developed to solve security issues and security threats in the world. It has become possible because of developments in technology in the fields of automatic face analysis, machine learning, and pattern recognition. This chapter proposes a prototype to classify brain stroke and face recognition using ML techniques in the medical image. Face recognition and classification of stroke through ML technique like Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning with Convolution Neural Network (CNN) based on texture features and statistical features. A CNN model is experimentally used along with a collection of hyper-parameters for deep face recognition and stroke classification. Promising experimental results, with a total accuracy of 98.86% and 98.5%, are obtained, which demonstrates the effectiveness of deep face identification and stroke classification, respectively, in biometric systems.

Weed identification techniques that are effective can lower the cost of weed control while also improving crop quality and output. Herbicides are currently the most frequent method of weed management. In order to deal with the issues of weed man-agement in agriculture, accurate crop and weed discrimination is essential. This work deals with the performance comparison of different classification algorithms namely Convolutional Neural Network(CNN), Graph Convolutional Networks(GCN), k-Nearest Neighbor(k-NN), and Support Vector Machine(SVM). Feature extraction algorithms, Local Directional Pattern(LDP) and Local Directional Relation Pattern(LDRP) are used alongside SVM and kNN. For data expansion, Generative Adversarial Network(GAN) was used, which generates synthetic samples of images from the dataset. The result of the study shows that CNN gives the maximum accuracy of 90.58% in ‘Crop and weed detection’ dataset when compared with other classification models used for investigation in our paper.

The main precondition for applications such as face recognition and face de-identification for privacy protection is efficient face detection in real scenes. In this paper, we propose a hybrid cascade model for face detection in the wild. The cascaded two-stage model is based on the fast normalized pixel difference (NPD) detector at the first stage, and a deep convolutional neural network (CNN) at the second stage. The outputs of the NPD detector are characterized by a very small number of false negative (FN) and a much higher number of false positive face (FP) detections. The FP detections are typically an order of magnitude higher than the FN ones. This very high number of FPs has a negative impact on recognition and/or de-identification processing time and on the naturalness of the de-identified images. To reduce the large number of FP face detections, a CNN is used at the second stage. The CNN is applied only on vague face region candidates obtained by the NPD detector that have an NPD score in the interval between two experimentally determined thresholds. The experimental results on the Annotated Faces in the Wild (AFW) test set and the Face Detection Dataset and Benchmark (FDDB) show that the hybrid cascade model significantly reduces the number of FP detections while the number of FN detections are only slightly increased.

In this paper, local directional position pattern (LDPP) and local ternary pattern (LTP) are selected for facial recognition method which are having many advantages over previous techniques like local binary pattern (LBP) and local directional pattern (LDP). The selected techniques of LDPP and LTP are estrangement in their algorithms which help solely to extract features out of an image. LDPP is a revised form of LDP. In a typical LDP, only the top edge direction was taken into consideration, but strength sign of the pixel was not considered which may result in same code for opposite kind of edge pixel. This snag is overcome by LDPP which is further concatenated with LTP for better feature extraction. Once features are extracted they are trained using deep belief network. In the experimental work 10 images of each expression i.e. angry, surprise, disgust, neutral, sad, smile are selected. LDPP and LTP are concatenated followed by principal component analysis (PCA) and general discriminant analysis (GDA). Further for training, Deep Belief Network (DBN) is used which eventually increases the recognition rate and achieve accuracy of 95.3% which was 89.3% without concatenating.

Local line directional pattern for palmprint recognition

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting compensation technique and we segment the skin color of the face image with the improved skin color model which is in the space of YCrCb, and then find the human face in a small area using the template matching method. The application on the skin color information is add to the face detection algorithm, the purpose of which is to pre-judge whether the image exist a face, thus we can exclude non-skin regions to reduce the space to search and improve the efficiency of the face detection algorithm. This paper proposed various adaptive templates to overcome the shortcomings of poor adaptability, the scale of the template can be seized to adjust the size of the area, so the adaptability of template can be increased.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.