D. S. Chauhan

Bio: D. S. Chauhan is an academic researcher from University Institute of Engineering and Technology, Panjab University. The author has contributed to research in topics: Face detection & Color histogram. The author has an hindex of 2, co-authored 2 publications receiving 337 citations.

A Robust Skin Color Based Face Detection Algorithm

Abstract: In this paper, a detailed experimental study of face detection algorithms based on ”Skin Color” has been made. Three color spaces, RGB, YCbCr and HSI are of main concern. We have compared the algorithms based on these color spaces and have combined them to get a new skin color based face detection algorithm which gives higher accuracy. Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 95.18%.

A comparison of face recognition algorithms neural network based & line based approaches

Abstract: One of the most successful applications of image analysis and understanding, face recognition has received significant attention. There are at least two reasons for the trend: the first is the wide range of commercial and law enforcement applications and the second is the availability of feasible technologies. In general, few methods of face recognition are in practice: feature based face recognition methods, eigen face based, line based, elastic bunch graph method and neural network based methods. All have their possibilities and features. In the neural network approach automatic detection of eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by hybrid neural network that combines unsupervised and supervised methods for finding structures and reducing classification errors respectively. The line-based is a type of image-based approach. It does not use any detailed biometric knowledge of the human face. These techniques use either the pixel-based bi-dimensional array representation of the entire face image or a set of transformed images or template sub-images of facial features as the image representation. An image-based metric such as correlation is then used to match the resulting image with the set of model images. In the context of image-based techniques, two approaches are there namely template-based and neural networks. In the template-based approach, the face is represented as a set of templates of the major facial features, which are then matched with the prototypical model face templates.

Hand tracking and gesture recognition system for human-computer interaction using low-cost hardware

Abstract: Human-Computer Interaction (HCI) exists ubiquitously in our daily lives. It is usually achieved by using a physical controller such as a mouse, keyboard or touch screen. It hinders Natural User Interface (NUI) as there is a strong barrier between the user and computer. There are various hand tracking systems available on the market, but they are complex and expensive. In this paper, we present the design and development of a robust marker-less hand/finger tracking and gesture recognition system using low-cost hardware. We propose a simple but efficient method that allows robust and fast hand tracking despite complex background and motion blur. Our system is able to translate the detected hands or gestures into different functional inputs and interfaces with other applications via several methods. It enables intuitive HCI and interactive motion gaming. We also developed sample applications that can utilize the inputs from the hand tracking system. Our results show that an intuitive HCI and motion gaming system can be achieved with minimum hardware requirements.

A smartphone-based driver safety monitoring system using data fusion.

Abstract: This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility. The safety monitoring process involves the fusion of attributes gathered from different sensors, including video, electrocardiography, photoplethysmography, temperature, and a three-axis accelerometer, that are assigned as input variables to an inference analysis framework. A Fuzzy Bayesian framework is designed to indicate the driver’s capability level and is updated continuously in real-time. The sensory data are transmitted via Bluetooth communication to the smartphone device. A fake incoming call warning service alerts the driver if his or her safety level is suspiciously compromised. Realistic testing of the system demonstrates the practical benefits of multiple features and their fusion in providing a more authentic and effective driver safety monitoring.