Face identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biometrics and accurate results. Face recognition utilizes facial features for security purposes. The classification method in this paper is K-nearest Neighbor (KNN). The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. K-NN includes an instance-based learning group. This paper developed face identification using Principal Component Analysis (PCA) or eigenface extraction methods. The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. This research is registered 30 people, each person had 3 images used for training and 2 images used for testing. The results obtained from several trials of k values are as follows. Experiments with a value of k=1 get the best accuracy, namely 81%, k=2 get 53% accuracy, and k=3 get 45% accuracy.

/pdf/face-identification-based-on-k-nearest-neighbor-357cpd2omn.pdf

Face Identification Based on K-Nearest Neighbor

Face recognition has been thought-provoking task in object detection, digital image processing and pattern recognition. Face recognition plays a significant role in realtime surveillance systems. In this paper, we present a comprehensive overview of the state of the art research work on face detection and recognition. Furthermore, the performance analysis of various face recognition approaches are discussed and conclude the paper with future research direction.

Performance Analysis of Human Face Recognition Techniques

Face recognition is a biometric based technology that maps an individual's facial features mathematically and stores the data as a face print. It employs Machine Learning on the image and generates a feature vector which maps an object with array of numbers. This technology is used by organizations such as Google and Facebook to create a digital profile for its users. This project proposes to use this technology for identifying criminals who are on the run from their previous records. An NCRB (National Crime Records Bureau) report shows that 70% of crimes are repeatedly committed by the same criminals. These criminals can be identified by the face recognition from an image or video frame which is captured by the cameras which are installed in various locations and it can also be used for identifying missing children. The disadvantage posed is that the images are usually blurred, have less clarity and not recognizable to the human eye. The proposed system can successfully recognize more than one face which is useful for quickly searching suspected persons as the computation time is very low. It creates a unique template for each face and compare them with other images available in dataset. If the match is found for the input face, then the details associated with the related image will be displayed. This system will decrease the crimes and ensure the security in our society.

Criminals And Missing Children Identification Using Face Recognition And Web Scrapping

Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.

/pdf/a-machine-learning-based-approach-for-counting-blister-cards-42clm7f6ob.pdf

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Recent advancements in the field of machine learning (ML) provide opportunity to conduct research on autonomous devices for a variety of applications. Intelligent decision-making is a critical task for self-driving systems. An attempt is made in this study to use a deep learning (DL) approach for the early detection of road cracks, potholes, and the yellow lane. The accuracy is not sufficient after training with the default model. To enhance accuracy, a convolutional neural network (CNN) model with 13 convolutional layers, a softmax layer as an output layer, and two fully connected layers (FCN) are constructed. In order to achieve the deeper propagation and to prevent saturation in the training phase, mish activation is employed in the first 12 layers with a rectified linear unit (ReLU) activation function. The upgraded CNN model performs better than the default CNN model in terms of accuracy. For the varied situation, a revised and enriched dataset for road cracks, potholes, and the yellow lane is created. The yellow lane is detected and tracked in order to move the unmanned aerial vehicle (UAV) autonomously by following yellow lane. After identifying a yellow lane, the UAV performs autonomous navigation while concurrently detecting road cracks and potholes using the robot operating system within the UAV. The performance model is benchmarked using performance measures, such as accuracy, sensitivity, F1-score, F2-score, and dice-coefficient, which demonstrate that the suggested technique produces better outcomes.

An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

Face recognition can be considered one of the most successful biometric identification methods among several types of biometric identification including fingerprints, DNA, palm print, hand geometry, iris recognition, retina and odour/scent. Face recognition provides biometric identification that utilizes the uniqueness of faces for security purposes. The problem with face recognition using biometric identification is its lengthy process and the accuracy of the results. This paper proposes solutions for a faster face recognition process with accurate results. The proposed face recognition process was done using a hybrid process of Haar Cascades and Eigenface methods, which can detect multiple faces (55 faces) in a single detection process. This improved face recognition approach was able to recognize multiple faces with 91.67% accuracy level.

Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods

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Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods