Hii...I am Nidhi. I like to implement face detection using haar classifiers in FPGA. I am struggling with reading xml file in Vivado from open cv for extracting features. Please help me with reading an xml file in Xilinx Vivado (either in VHDL or In C) software from opencv.
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A facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features of the image and a face database. It is often used in security systems and can be compared with other biometric data such as fingerprint or iris eye recognition systems. Recently, it has also become popular as a business identification and marketing tool.
Face recognition (or facial recognition) is a biometric method of identifying an individual by comparing the live capture or digital image data with the record stored for that individual.
Facial recognition systems are commonly used for safety purposes, but are increasingly used in a variety of other applications. The Kinect motion gaming system, for example, uses facial recognition to differentiate between players. Some mobile payment systems use facial recognition to securely authenticate users, and facial recognition systems are being studied or deployed for airport security.
Most current face recognition systems work with numerical codes called faceprints. Such systems identify 80 nodal points on a human face. In this context, nodal points are endpoints used to measure variables on a person's face, such as the length or width of the nose, the depth of the holes, and the shape of the cheekbones. These systems work by capturing data for nodal points on a digital image of an individual's face and storing the resulting data as a facial print. The facial face can then be used as the basis for comparison with captured face data in an image or video.
Face recognition systems based on facial faces can quickly and accurately identify target individuals when conditions are favorable. However, if the subject's face is partially obscured or profiled rather than looking forward, or if the light is insufficient, the software is less reliable. However, technology is evolving rapidly and there are several emerging approaches, such as 3D modeling, that can overcome current problems with systems. According to the National Institute of Standards and Technology (NIST), the incidence of false positives in facial recognition systems has been halved every two years since 1993 and by the end of 2011 was 0.003%
At present, a lot of facial recognition development focuses on smartphone applications. Smartphone facial recognition capabilities include image tagging and other social networking integration purposes as well as custom marketing. A Carnegie Mellon research team has developed a proof-of-concept iPhone application that can take a photo of an individual and - within seconds - return the individual's name, date of birth and social security number.
