Safe Security System Using Face Recognition Based on IoT

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.

In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate.

Facial recognition is a challenging research in the field of image processing and computer vision, especially for security systems, weight determiner, and emotional determination based on the face image recognition. Some of the methods that can be used in facial recognition are holistic, feature extraction, hybrids and intelligent systems. This paper used the method of characteristic extraction that used Principal Component Analysis (PCA) which was built using EmguCV application. The purpose of this research is to assess the accuracy of Principal Component Analysis (PCA) method when combined with Emgu CV in face recognition in real time. Based on the results of training and testing, it can be concluded that the PCA method combined with EmguCV has better accuracy, if the data used has the same characteristics, PCA and EmguCV can also be developed to make image processing application especially for security system, because it applies simple statistic method and easy- applied algorithm.

Lots of people in this world lack electricity and modern lighting. This problem is severe in rural areas. In most schools, houses, and offices, the management of the lighting system is done manually as per requirement. As the world is advancing, automation develops and spreading everywhere. In this paper, an automatic light switch device is controlled by face recognition. Face Recognition Automation (FRA) is a biometric recognition technology for processing information about facial features. Like other automation, it has the advantage: Faster processing of recognizing a face takes a second or less. The studies provide development and advancement of automation control in the individual room. The primary purpose of this paper is to improve automation control in the designated room. This paper aims to develop an automated program of accessibility in outlets and switching ON/OFF of lights. Having an auto switch and auto access by a face recognition control results in greater safety, flexibility, reliability, and much more precise data. The implementation of the entire Automatic lighting control system has been carried out using Raspberry PI 3B+. The software for controlling the relay switch for switching on and off the light based on the input from the sensor resides inside the microcontroller unit of Raspberry PI 3B+. The code for a sample node has was developed and executed. In operating, the user will just look in-front of the camera and let the camera recognize the pickles and send data to the Raspberry Pi 3b+ and then send a wavelength to the relay that makes lights and outlet on/off. Therefore, Face recognition is one of the best ways of operating that is invented in terms of automation. The face recognition system does not need physical contact with any machine upon operating, and Face recognition is easy to operate, especially for disabled people. The researchers encounter many errors in doing the research. Number one is the delay recognition and Multiple functions of switching. However, the system could be enhanced by replacing more power full camera and powerful board.

Face detection entails the technology that identifies the human face in digital images. Face detection can be used in various applications. One of the major areas of application of face recognition is in security systems. We know that a smart security system is the need of the hour and has become a necessity in today’s time. It is the basic need of humans to be able to protect one’s home and belongings. The security system needs to be smart and efficient. Hence, it avoids intruders and protects your possessions by minimizing break-ins. Installing a home security system helps in protecting your home and valuables from burglars. The security system developed has a camera module that will give access to only authenticated persons to unlock the main door of the house using facial recognition technology. This discrete system is developed using an electronic board called as Raspberry Pi can detect intruders as well as send off an alarm to nearby police stations using SMS or Email. Keywords: Home security; Face Recognition; Internet of Things (IoT); RaspberryPi Microcontroller; Control; Smartphones Cite this Article Ayush Yadav, Shaam Garg, Shivam Jamloki, Aanchal Sadana, Kusum Tharani. Home Security using Facial Recognition. Current Trends in Signal Processing. 2020; 10(2): 5–11p.

Face recognition has numerous applications in the modern world. With recent developments in IoT devices, security, and biometric systems, many applications of face recognition are being used on devices like a raspberry pi. In this paper, we propose an efficient face recognition system that uses face detection and extraction of the face from image based on Single Shot Multibox Detector (SSD) and uses image enhancement techniques like bilateral filtering and histogram equalization to enhance the quality of face image after which Principal Component Analysis (PCA) is used for feature extraction and Linear Discriminant Analysis (LDA) is used as classifier. The experiments have been conducted on the Faces95 and Faces96 datasets to test the proposed system and the performance of the system is also compared with two other methods for face recognition namely LBPH and PCA with SVM classifier. The testing of the system in real-time shows great results while recognizing faces.

Face Recognition is a prototype with stirring functions in the direction of facial features for consent and validation. In this approach, a particular type of face recognition systems is bond different feature extraction approaches to conquer the image entity configuration. To solve face recognition under unreliable pose is rigorous pose invariant features with effective approach. In this work, an algorithm is implemented in MATLAB and performance evaluation of achievement rate with existing techniques is proposed. Also, the performance of the face recognition system is based on feature extractions using wavelet transform and demonstrates the improved quality image. This work provides Artificial Neural Network (ANN) for face recognition and using a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) algorithm for feature extraction and acquires a transformed face image to raise the features. The results demonstrated that the proposed system has higher accuracy, precision and sensitivity than face recognition using different feature extractors.

Mobile devices and mobile computing play an important role in the day to day life of people. Nowadays mobile devices are capable of doing very complex applications. But because of the resource scarcity of mobile devices, the compute intensive applications, like computer vision applications, are still found to be very difficult to execute on mobile devices. As a solution cyber foraging, where mobile devices offload either computation or data or both to a device within their network, can be applied. This paper explains our work in which we have successfully implemented a principal component analysis(PCA) based face recognition using VMBased cloudlet. In the face detection phase we are using a skin color based approach to extract the face region by which the accuracy and speed of the application is found to be increased.

The equivalence of two-dimensional PCA to line-based PCA

Face recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal component analysis (PCA) is a classical and successful method for face recognition. Self organizing map (SOM) has also been used for face space representation. This paper makes an attempt to integrate the two techniques for dimensionality reduction and feature extraction and to see the performance when the two are combined. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. The advantage in combining the two techniques is that the reduction in data is higher but at the cost of recognition rate

Principal component analysis (PCA) has been proven to be an efficient method in pattern recognition and image analysis. Recently, PCA has been extensively employed for face-recognition algorithms, such as eigenface and fisherface. The encouraging results have been reported and discussed in the literature. Many PCA-based face-recognition systems have also been developed in the last decade. However, existing PCA-based face-recognition systems are hard to scale up because of the computational cost and memory-requirement burden. To overcome this limitation, an incremental approach is usually adopted. Incremental PCA (IPCA) methods have been studied for many years in the machine-learning community. The major limitation of existing IPCA methods is that there is no guarantee on the approximation error. In view of this limitation, this paper proposes a new IPCA method based on the idea of a singular value decomposition (SVD) updating algorithm, namely an SVD updating-based IPCA (SVDU-IPCA) algorithm. In the proposed SVDU-IPCA algorithm, we have mathematically proved that the approximation error is bounded. A complexity analysis on the proposed method is also presented. Another characteristic of the proposed SVDU-IPCA algorithm is that it can be easily extended to a kernel version. The proposed method has been evaluated using available public databases, namely FERET, AR, and Yale B, and applied to existing face-recognition algorithms. Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%. This implies that the proposed SVDU-IPCA method gives a close approximation to the batch-mode PCA method.

Perturbation analysis on PCA plus graph embedding methods and PCA plus exponential graph embedding methods

Automatic facial feature extraction is one of the most important and attempted problems in computer vision. It is a necessary step in face recognition, facial image compression. There are many methods have been proposed in the literature for the facial feature extraction task. However, all of them have still disadvantage such as not complete reflection about face structure, face texture. Therefore, a combination of different feature extraction methods which can integrate the complementary information should lead to improve the efficiency of feature extraction stage. In this paper we describe a methodology for improving the efficiency of feature extraction stage based on the association of two methods: geometric feature based method and Independent Component Analysis (ICA) method. Comparison of two methods of facial feature extraction: geometric feature based method combined with PCA method (called GPCA) versus geometric feature based method combined with ICA method (called GICA) on CalTech dataset has demonstrated the efficiency of GICA method. Our results show that GICA achieved good performance 96.57% compared to 94.70% of GPCA method. Furthermore, we compare two methods mentioned above on our dataset, with performance of GICA being 98.94% better 96.78% of GPCA method. The experiment results have confirmed the benefits of the association geometric feature based method and ICA method in facial feature extraction.KeywordsFace recognitionindependent component analysis (ICA)principal component analysis (PCA)geometric features

Face recognition is widely used as identity authentication method in the Internet of Things (IoTs). However, compared with static camera, the background of IoT devices may change. Additionally, the facial expression or posture may change as well. Although face recognition has been researched for several decades, it is still a challenging task to design a robust face recognition system in IoT environment. This paper designs a special face recognition system by introducing deep transfer learning to adapt difference scenes in IoT environment. The face recognition system consists of two parts: terminal smart devices and cloud center. The terminal smart device consists of a camera to capture face picture, a wireless network part to communicate with cloud center, and a screen to show recognition result. The cloud center receives the face picture from terminal smart device and returns the face recognition result by using an offline deep transfer learning model. Compared with previous models, deep transfer learning model can better adapt different scenes. The experiments shows that the proposed face recognition system achieves more promising results than two source‐only models and two shallow transfer learning models.