Implementation of Template Matching Method for Door Lock Security System Using Raspberry Pi

In several locations, the security system is becoming a priority. One of the location is the laboratory. There are many valuable research tools and equipment in the laboratory, so to improve the security system. Generally, the access to entry the laboratory is equipped with a digital safety system. Computer Control System Laboratory at Information Technology Department of Jember State Polytechnic uses RFID-Card as access right for entrance to the laboratory. RFID-Card also functions as ID-Card (printed with photo and biodata) and packaged to fit in user clothes pocket. However, using RFID gives less of flexibility. The user must remove the ID-Card and attach the card to the RFID reader box. Therefore, in this study will design a door lock security system based on the image processing to replace the use of RFID. Later, the user simply positions themselves in the Pi Camera work area. Processed data from the camera, the matched with image data which stored in the database. The database contains a photo of laboratory members which listed on the employee ID-Card. Template matching method is used as image matching method which stored in Raspberry Pi database. If the image which captured by camera is matched with the image data in the database, the controller will give entry access to the user. Based on the experimental results, template matching method has a success rate of 96%

Secure Access To Authorized Resources Based On Fingerprint Authentication

The Internet makes it convenient for anyone to access publicly available information on the servers. The increased functionality of computers and other technological equipment makes the connectivity easier than ever before, and the need for security more and more important. Passwords are frequently used to control access to restricted functions. Unfortunately, password or personal identification number (PIN) verification suffers an inherent problem. It cannot ensure that the user is the claimed individual. Higher security systems have now veered towards biometric verification in conjunction with passwords. Fingerprints are a practical bodily characteristic to use, as they are unique to each individual and easily collected using image-capture systems. In this thesis, the methods of fingerprint recognition and classification are investigated. Then, the possible approaches to use are discussed while investigating the subject. The final choice combines a feature-based and correlation-based approach. The thesis proposes a novel method of allowing users access after authenticating them by their fingerprints. The method is based on a statistical approach, and is crafted in such a way that the authentication operations are inconspicuous to the user. All the required image processing techniques that make the extraction of the true fingerprint features easier are used: equalization, filtering, binarization and thinning. A new method for constructing a unique key from the fingerprint image is also presented, with the fingerprint database (fingerprint features, unique fingerprint key, public and private keys) created shown. The database can be manipulated (insertion, retrieving, and deletion) rapidly using the Adelson Velskii and Landis (AVL) tree searching technique. The A VL tree is used to increase the compression ratio as its compression algorithm works efficiently for all types of data. The security is maintained and enhanced by adding a public key system. Finally, a unique fingerprint based key is constructed using a user's fingerprint image and the key used to access and retrieve the private key. The method successfully recognizes the user fingerprint image even with noisy, translated or rotated images

Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation

International audienceRecent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner

The Effect of Database Type on Face Recognition Performance for Surveillance Applications

Face recognition is one of the most important biometric approaches due to its potential applications in surveillance monitoring and access control. This paper presents a PCA and SVM based face recognition system for surveillance application. A proposed training database selection criteria suitable for surveillance application which consist of 1 mean image per distance class from all the available database sessions is also used for the face recognition system. In this study, the ChokePoint database, specifically the grayscale (PPG) and colored (MPCI) versions of the ChokePoint database, were selected for this work. The objectives of this work is to investigate the effect of the using different training data as well as using different similarity matching method on face recognition for surveillance application. It was found that regardless of the type of databases used, the recognition output pattern on different training data selection criteria was found to be similar. It was also found that regardless of the similarity matching method used, the face recognition system also shows the same recognition performance pattern. The experiment suggests that the proposed training database selection criteria will give similar recognition performance regardless of databases type or face recognition technique used. Overall, the ChokePoint colour database (MPCI) gives better recognition performance than the ChokePoint grayscale database (PPG). Finally, it can be concluded that using 1 mean image per class from all the available database sessions (Case-6) is better compared to using 1 image per class that are randomly selected from all the database sessions (Case-4). Even though a straight comparison between this work proposed system and several published system is not meaningful as different face recognition approaches and experiment criteria are used, nevertheless, this work proposed method performs with 100% recall and reject recognition rate

METHOD AND SYSTEM FOR ESTABLISHING CARDLESS ATM AUTHENTICATION AND VALIDATION USING FACE DETECTION

The present disclosure relates to a method and system for card-less Automated Teller Machine (ATM) authentication and validation using face detection. The cardholder\u27s image is captured using the ATM\u27s built-in cameras. Determine the weather condition at the present location obtained from National Weather Services and correlate with the CCTV captured image. Thereafter compare the CCTV captured image with the user data stored in a database. Further, verify the user movements by using a motion detection method, and based on verification, user is prompted to enter a PIN number to authenticate the transaction. Based on the PIN number authentication, the user is allowed to access cash or other possible services. Allowing card-less/phoneless ATM transactions assist the user to get access to their money even if they have misplaced their cards, phone, or wallet

Acquisition of Images using Neural Network

The application of computer vision to the image retrieval problem is Content-based image retrieval (CBIR). The interest in digital images is growing day by day. Users in professional fields are make use of the opportunities offered by the ability to access and manipulate remotely-stored images in different ways. The problems in image retrieval are becoming widely accepted, and the finding solution is an active area for research and development. This dissertation work aims at developing a hybrid scheme for intelligent image retrieval system using neural networks. Each image in the database is indexed by a visual feature vector, which is extracted using color moments and discrete cosine transform coefficients. The query is characterized by a set of predefined semantic labels. A novel method of similarity measure using dot product is used for ranking and retrieval for improved performance of the system DOI: 10.17762/ijritcc2321-8169.15050