Abstract: The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.
Abstract: This paper proposes a VPN Accelerator Board
(VPN-AB), a virtual private network (VPN) protocol designed for
trust channel security system (TCSS). TCSS supports safety
communication channel between security nodes in internet. It
furnishes authentication, confidentiality, integrity, and access control
to security node to transmit data packets with IPsec protocol. TCSS
consists of internet key exchange block, security association block,
and IPsec engine block. The internet key exchange block negotiates
crypto algorithm and key used in IPsec engine block. Security
Association blocks setting-up and manages security association
information. IPsec engine block treats IPsec packets and consists of
networking functions for communication. The IPsec engine block
should be embodied by H/W and in-line mode transaction for high
speed IPsec processing. Our VPN-AB is implemented with high speed
security processor that supports many cryptographic algorithms and
in-line mode. We evaluate a small TCSS communication environment,
and measure a performance of VPN-AB in the environment. The
experiment results show that VPN-AB gets a performance throughput
of maximum 15.645Gbps when we set the IPsec protocol with
3DES-HMAC-MD5 tunnel mode.
Abstract: To increase reliability of face recognition system, the
system must be able to distinguish real face from a copy of face such
as a photograph. In this paper, we propose a fast and memory efficient
method of live face detection for embedded face recognition system,
based on the analysis of the movement of the eyes. We detect eyes in
sequential input images and calculate variation of each eye region to
determine whether the input face is a real face or not. Experimental
results show that the proposed approach is competitive and promising
for live face detection.