Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

Video-based driver identification using local appearance face recognition

In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms — Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1

Face Recognition System to RPA Software Design and Implementation

The purpose of this research is to build a face recognition system, and implement it into an RPA (Robotic Process Automation) software to expand automation capabilities. The system is built using the Python programming language. The face recognition algorithm that is used is an open-source library that has been pre-trained and developed beforehand along with a library called OpenCV. The client side of the system is desktop based, and requires a stable internet connection. Users of the system are able to register faces into the system, and then later detect and extract information from them using only images of faces with an average speed of 500 ms for every frame, with an accuracy of ~98% with tolerance set at the default value of 0.6. The system is also capable of automatically registering any new faces that it encounters

Pelacakan Dan Estimasi Pose Video Wajah 3 Dimensi

The paper describes a face tracking and pose estimation system by using a webcamera. A 3D wireframe face model was used in conjunction with a feature tracking method of Lucas-Kanade. A set of face feature points was tracked in every video frame by using Lucas-Kanade method. A fully projective method was depployed for face pose estimation. This application was built using Microsoft Visual C 6.0®%2C Microsoft® DirectShow®%2C Intel Performance Library and Open Source Computer Vision (OpenCV) Library. The application has been implemented and able to track the face movement in real time (30 frames/second)%2C using webcam with the resolution of 320?240 pixel/frame on PC Pentium III/533 MHz. The system is prospective to be used for human-computer interaction applications as well as face expression recognition system

Multi-modal Open-Set Person Identification in HRI

In this paper, we describe a multi-modal Bayesian network for person recognition in a HRI context, combining information about a person's face, gender, age, and height estimates, with the time of interaction. We conduct an initial study with 14 participants over a four-week period to validate the system and learn the optimal weights for each of the metrics. Several normalisation methods are compared for different settings, such as learning from data, face recognition threshold and quality of the estimation. The results show that the proposed network improves the overall recognition rate by at least 1.4% comparing to person recognition based on face only in an open-set identification problem, and at least 4.4% in a closed-set

Software Support for Skeleton Recognition and Monitoring People with Privacy

In this research, we have developed an open source software tool which makes it easy for a user to get skeleton information of people in a live image by use of Kinect for Windows V2. Our tool is a set of library software which provides users with easy coding to get body information and face recognition. The tool has been distributed widely and used by several users already for their research work such as robotics. In this paper, we propose possible use cases such as a remote monitor system for elderly care with privacy as well as a monitor system for shelters at disaster

An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

The problem of training a deep neural network with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications are those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research in this area, results with two baseline systems (one trained from scratch and another based on transfer learning), are presented.Comment: To be submitted to Expert System with Application