Abstract: Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.
Abstract: Skin color can provide a useful and robust cue
for human-related image analysis, such as face detection,
pornographic image filtering, hand detection and tracking,
people retrieval in databases and Internet, etc. The major
problem of such kinds of skin color detection algorithms is
that it is time consuming and hence cannot be applied to a real
time system. To overcome this problem, we introduce a new
fast technique for skin detection which can be applied in a real
time system. In this technique, instead of testing each image
pixel to label it as skin or non-skin (as in classic techniques),
we skip a set of pixels. The reason of the skipping process is
the high probability that neighbors of the skin color pixels are
also skin pixels, especially in adult images and vise versa. The
proposed method can rapidly detect skin and non-skin color
pixels, which in turn dramatically reduce the CPU time
required for the protection process. Since many fast detection
techniques are based on image resizing, we apply our
proposed pixel skipping technique with image resizing to
obtain better results. The performance evaluation of the
proposed skipping and hybrid techniques in terms of the
measured CPU time is presented. Experimental results
demonstrate that the proposed methods achieve better result
than the relevant classic method.
Abstract: In the last years numerous applications of Human-
Computer Interaction have exploited the capabilities of Time-of-
Flight cameras for achieving more and more comfortable and precise
interactions. In particular, gesture recognition is one of the most active
fields. This work presents a new method for interacting with a virtual
object in a 3D space. Our approach is based on the fusion of depth
data, supplied by a ToF camera, with color information, supplied
by a HD webcam. The hand detection procedure does not require
any learning phase and is able to concurrently manage gestures of
two hands. The system is robust to the presence in the scene of
other objects or people, thanks to the use of the Kalman filter for
maintaining the tracking of the hands.
Abstract: In this paper we proposed a novel method to acquire
the ROI (Region of interest) of unsupervised and touch-less palmprint
captured from a web camera in real-time. We use Viola-Jones
approach and skin model to get the target area in real time. Then an
innovative course-to-fine approach to detect the key points on the hand
is described. A new algorithm is used to find the candidate key points
coarsely and quickly. In finely stage, we verify the hand key points
with the shape context descriptor. To make the user much comfortable,
it can process the hand image with different poses, even the hand is
closed. Experiments show promising result by using the proposed
method in various conditions.