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This paper presents a series of experiments on the classification of emergency phone conversation records using artificial neural networks (ANNs). Input data which were processed by ANNs were the features of callers and events taken from emergency phone calls. The authors analyzed four variants of classification: the groups of callers which have specified features, the groups of events which have...
This paper provides a voice transformation model that uses pitch data and Feed-forward Neural Networks on Line Spectral Frequency. The aim of this work is to achieve the transformation of a speech signal produced by a source speaker by modifying voice individuality parameters such that it appears to be spoken by a chosen target speaker, without modifying the message contents. Most of the previous...
Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS — achieving the position estimation by reusing the available WLAN and mobile devices, and is...
The major challenges for optical based tracking are the lighting condition, the similarity of the scene, and the position of the camera. This paper demonstrates that under such conditions, the positioning accuracy of Google's Tango platform may deteriorate from fine-grained centimetre level to metre level. The paper proposes a particle filter based approach to fuse the WiFi signal and the magnetic...
Due to lack of GPS signals' indoor coverage, the implementation of Signals of Opportunity (SoOp) such as FM signals, Wi-Fi, Bluetooth, RFID, and Cellular Networks for indoor localization has been considered. Not all SoOp are good performers because of some constraints; for instance, lack of accuracy, higher cost in deployment, additional hardware requirements. In this research, a new method of indoor...
We propose a dialogic system based on a relevance feedback strategy that allows for the semiautomatic synthesis of a facial image that only exists in a user's mind. The user is presented with several facial images and judges whether each one resembles the face that he or she is imagining. Based on the feedback from the user, a set of sample facial images are used to train an Optimum-Path Forest classifying...
This paper introduces an efficient probabilistic approach with RSSI fingerprinting for Indoor Localization. A Shannon's Entropy based access points (APs) selection is considered. Once the APs selection is performed, a probability is assigned to each training fingerprint based on RSSI measurements. Then, the user's location is estimated as a combination of training positions weighted with their corresponding...
The continuing success of synchrophasors has ushered in new subdomains of power system applications for real-time situational awareness, online decision support, and robust system control. In this paper, an adaptive decision-tree-based systematic method for open-loop regional voltage control is developed. This approach employs voltage security assessment method to generate voltage secure and insecure...
Classification of benign and malignant masses in mammograms is one of the most difficult tasks in development of mammographic computer-aided diagnosis (CAD) system. This paper presents a deep learning-based method that utilizes a deep convolutional neural network (DCNN) to classify mammographic masses into two classes: benign and malignant masses. In order to train the DCNN for mass classification,...
Automatic recognition of human demographical attributes has implications in a variety of domains, such as surveillance systems, human computer interaction, marketing etc. In this paper, we present an automatic gender recognition method from facial images based on convolutional neural networks. In order to train the network, we merged together several face databases and also gathered and annotated...
Machine learning has been a detection technique used by many security vendors for some time now. With the enhancement brought by GPUs, many security products can now use different deep learning methods and forms of neural networks for malware classification. However, these new methods, as powerful as they are, are also limited by the amount of memory a GPU has or by the constant need of transferring...
The importance of face anti-spoofing algorithms in biometric authentication systems is becoming indispensable. Recently, the success of Convolution Neural Networks (CNN) in key application areas of computer vision has encouraged its use in face biometrics for face anti-spoofing and verification applications. However, small training data has restricted the use of deep CNN architectures for face anti-spoofing...
The bank client identity verification system developed in the course of the IDENT project is presented. The total number of five biometric modalities including: dynamic signature proofing, voice recognition, face image verification, face contour extraction and hand blood vessels distribution comparison have been developed and studied. The experimental data were acquired employing multiple biometric...
This paper proposes a low-cost video-based Real-Time Pupil-Tracking embedded system which will allow people with reduced mobility to control a wheelchair through their eyes. The main aspect of the method is its capacity to be implemented in a portable computing system, reduced both in computing power and in RAM memory. The Pupil-Tracking system is based on Feedforward Neural Networks-using offline...
This letter presents a prototype discriminative learning (PDL) method for image set classification. We aim to simultaneously learn prototypes and a linear discriminative projection to drive that in the target subspace each image set can be discriminated with its nearest neighbor prototype. To reveal the unseen appearance variations implicitly in an image set, the prototypes are actually “virtual,”...
A face recognition system which represents each image as a superposition of the dominant components in two transform domains is proposed. The Discrete Wavelet Transform (DWT) and the Discrete Cosine Transform (DCT) are the two domains. By the end of the Training mode, each pose in the gallery will have two final matrices. Feature Extraction step in the Training includes transforming the preprocessed...
Command extraction from human beings becomes easier for a machine if it can analyze the non verbal ways of communication such as emotions. This paper focuses on improving the efficiency of extracting emotion from human facial expression images. The features that were extracted in this experiment were obtained from JAFFE (Japanese Female Facial Expression) database which includes 213 images of different...
The present work proposes to recognize the static hand gestures taken under invariations features as scale, rotation, translation, illumination, noise and background. We use the alphabet of sign language of Peru (LSP). For this purpose, digital image processing techniques are used to eliminate or reduce noise, to improve the contrast under a variant illumination, to separate the hand from the background...
License Plate Detection (LPD) is the pivotal step for License Plate Recognition. In this work, we explore and customize state-of-the-art detection approaches for exclusively handling the LPD in the wild. In-the-wild LPD considers license plates captured in challenging conditions caused by bad weathers, lighting, traffics, and other factors. As conventional methods failed to handle these inevitable...
Recent work in the recognition of naturalistic expressions, which is also known as spontaneous facial expressions recognition, has attracted researchers' attention due to its importance in different behavioural and clinical applications. The main design challenges in the area of emotion computing for automatic recognition of spontaneous facial expression are the face pose, capture distance, illumination...
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