License Plate Recognition Technology Development Research and Improvement

License plate recognition technology is an important part of an intelligent transport system, widely used in highway tolls, unregistered vehicle monitoring, vehicle parking management, and other important occasions. Typical of the license plate recognition, algorithm is divided into three components, license plate localization, character segmentation, and character recognition. This paper summarizes the key technology of license plate recognition algorithm, and analyses the difficulties of improving the recognition rate. According to features of license plates, license plate character recognition methods in recent years were summarized and put forward, on the basis of the existing methods, improving system performance and accuracy

Robust human detection with occlusion handling by fusion of thermal and depth images from mobile robot

In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages, pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations

Pembangunan Aplikasi Identifikasi Waktu Kawin Ternak Babi dengan Alihragam Wavelet dan Backpropagation

Abstract.Pigs are one of the animals that are usually bred for pork production. In order to have the pork production which meet the high level of consumption, it is necessary to handle the mating time for the nursery process so that pigs can mate at the right time to avoid problems such as pregnancy failure. Determination of the right mating time in pigs can be decided by observing the shape and color of female pig vulva that is opened and slimy. The uniqueness of vulva in this breeding season can be analyzed and identified by using Haar Wavelet and Backpropagation neural networks. Wavelets are used to perform image feature retrieval, while Backpropagation artificial neural networks are used to create a base of knowledge to decide whether the pigs are at the right mating time. This application is a web application built by Visual Studio 2015 MVC5 framework with C # programming language. Users can use this application by using the Android application. The construction of this application aims to enable pig breeder to get the right time of mating to increase the pork production. This application will also be made for the smart phone version in order to make the detection process with this application more efficient.Keywords: Pattern Recognition, Haar Wavelet, Backpropagation, Pig Breeding.Abstrak. Babi merupakan hewan yang diternakkan untuk dimanfaatkan dagingnya. Agar produksi daging babi dapat memenuhi tingkat konsumsi yang tinggi, diperlukan penanganan waktu kawin agar babi dapat kawin pada waktu yang tepat sehingga menghindari permasalahan seperti kegagalan kebuntingan.Penentuan waktu kawin yang tepat pada babi terlihat pada bentuk dan warna vulva babi betina yang terlihat terbuka dan berlendir. Keunikan vulva ini dapat dianalisis dan dikenali polanya menggunakan alihragam Haar Wavelet dan jaringan syaraf tiruan Backpropagation. Wavelet digunakan untuk pengambilan ciri citra, jaringan syaraf tiruan Backpropagation digunakan untuk membuat basis pengetahuan untuk mengkategorikan karakter yang membuat babi dinyatakan berada dalam waktu kawin yang tepat. Aplikasi ini merupakan aplikasi web yang dibangun dengan bahasa pemrograman C#. Dibangunnya aplikasi ini bertujuan agar peternak babi dapat mendapatkan waktu yang tepat untuk mengawinkan ternaknya agar produksi babi dapat meningkat. Aplikasi ini juga dibuat pada ponsel pintar agar proses pendeteksian dengan aplikasi ini menjadi lebih efisien.Kata Kunci: Pengenalan Pola, Haar Wavelet, Backpropagation, Ternak Babi

ANALYSIS OF APPLICATION HAAR CASCADE CLASSIFIER AND LOCAL BINARY PATTERN HISTOGRAM ALGORITHM IN RECOGNIZING FACES WITH REAL-TIME GRAYSCALE IMAGES USING OPENCV

Face detection and recognition systems have been developed with the various application of algorithms. Based on the literature study that has been carried out, the researcher will analyze the performance between the HCC (Haar Cascade Classifier) ​​and LBPH (Local Binary Pattern Histogram) Algorithms with real-time grayscale images using the OpenCV library. The test is carried out based on a sample of facial images with external conditions in the form of lighting conditions which are divided into morning, afternoon, and evening, as well as varying face rotation angles. Parameters observed were accuracy values, FAR (False Acceptance Rate), and FRR (False Rejection Rate). Based on the results of the tests that have been carried out, the average value of accuracy is 56%, while the average value of FAR is 22% and FRR is 23%. Judging from the average accuracy value obtained is 56%, then to be able to be detected and recognized properly the face position must be in frontal condition and with normal lighting. Thus, the final results of this study can be considered for other researchers who want to use a similar algorithm to develop a detection and recognition system

Gradient edge map features for frontal face recognition under extreme illumination changes

Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6–7%

Prototype of Student Attendance Application Based on Face Recognition Using Eigenface Algorithm

Prototype of face recognition based attendance application that has been developed to overcome weaknesses in DTETI UGM student manual attendance system has several weaknesses. These weaknesses are a decrease in facial recognition accuracy when operating under conditions of varying environmental light intensity and in condition of face rotating towards z axis rotation centre. In addition, application prototype also does not yet have a database to store attendance results. In this paper, a new application prototype has been developed using Eigenface face detection and recognition algorithm and Haar-based Cascade Classifier. Meanwhile, to overcome prototype performance weaknesses of the previously developed application, a pre-processing method was proposed in another study was added. Processes in the method were geometry transformation, histogram levelling separately, image smoothing using bilateral filtering, and elliptical masking. The test results showed that in the category of various environmental light intensity conditions, face recognition accuracy from developed application prototypes was 16.71% better than previous application prototypes. Meanwhile, in category of face slope conditions at z axis rotation centre, face recognition accuracy from developed application prototype was 38.47% better. Attendance database system was also successfully implemented and running without error

Towards better performance: phase congruency based face recognition

Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing alow-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associate dangles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation,three competitive classifiers:  Euclidean distance (ED),cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental  results show promising recognition rates with a reasonable matching time

Text-based Emotion Aware Recommender

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.Comment: 13 pages, 8 tables, International Conference on Natural Language Computing and AI (NLCAI2020) July25-26, London, United Kingdo

SVM Based Approach for Multiface Detection and Recognition in Static Images

Recognizing and identifying a face from the real world, capture data that senses images is the demanding process in this advanced world. Because of varied face appearances, lighting effects and illumination of the background of the images, perceiving and recognizing multiple faces in a single image is a challenging process. This paper proposes a method that recognizes multiple faces in a single image using a different face recognition algorithm. Here, different approaches of face recognition using OpenCV and SVM algorithm have been compared and implemented for recognizing the multiple faces in a single image. In this method, the Haar Cascade Classifier, which is given by Viola Jones is used to detect the multiple faces in a single image. Local binary pattern histogram, eigenfaces and fisherfaces and Support Vector Machine learning algorithms are used to recognize multiple faces in a single image. These multiple face recognition algorithms are compared and tested over a different set of images