Face Detection on Embedded Systems

Over recent years automated face detection and recognition (FDR) have gained significant attention from the commercial and research sectors. This paper presents an embedded face detection solution aimed at addressing the real-time image processing requirements within a wide range of applications. As face detection is a computationally intensive task, an embedded solution would give rise to opportunities for discrete economical devices that could be applied and integrated into a vast majority of applications. This work focuses on the use of FPGAs as the embedded prototyping technology where the thread of execution is carried out on an embedded soft-core processor. Custom instructions have been utilized as a means of applying software/hardware partitioning through which the computational bottlenecks are moved to hardware. A speedup by a factor of 110 was achieved from employing custom instructions and software optimizations

Recognizing Faces from the Eyes Only

The eyes are one of the most important facial features for recognizing human faces. Many face recognition systems today make use of local features (such as eyes) for identification or verification of individuals, but no system to our knowledge has studied performance when the only available information is the eyes. In this paper we show that we can obtain 85% correct classification on the popular ORL face database, when the features are extracted from the eye area only. We compare feature extraction from eigenfeatures and Gabor wavelets with features consisting of simple graylevel pixel values. 1 Introduction Face recognition is a complex task which has recieved a great amount of attention in recent years, mostly due to its wide range of application in the area of biometric systems. Many different approaches have been proposed, and current systems exhibit very good performance on detection, identification and verification of human faces [10, 2]. However, one of the remain..

Quantitative analysis of efficient antispam techniques

While dynamic content-based filtering mechanisms for the identification of unsolicited commercial email (UCE, or more commonly \spam") have proven to be effective, these techniques require considerable computational resources. It is therefore highly desirable to reduce the number of emails that must be subjected to a content-based analysis. In this paper, a number of efficient techniques based on lower protocol level properties are analyzed using a large real-world data set. We show that combinations of several network-based filters can provide a computationally efficient pre-filtering mechanism at acceptable false-positive rates