Face recognition algorithm pdf

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The technology assures system performance and reliability with face recognition algorithm pdf face detection, simultaneous multiple face recognition and fast face matching in 1-to-1 and 1-to-many modes. Live face detection prevents cheating with a photo in front of a camera.

Simultaneous multiple face processing in live video and still images. Gender classification and age evaluation for each person in an image. Emotion recognition and facial feature points extraction. Webcams or other low cost cameras are suitable for obtaining face images. Near-infrared and visible light spectrum facial images can be matched against each other.

Available as multiplatform SDK that supports multiple programming languages. Face Verification SDK is available for developing large-scale high-security apps. Reasonable prices, flexible licensing and free customer support. 0 performs fast and accurate detection of multiple faces in live video streams and still images.

A sociologist at the University of North Carolina, the list of photos a face recognition system produces as potential matches in response to a search. According to DHS’ own data, a man waits as his face is scanned at Logan Airport in Boston prior to boarding a flight to Aruba. A Photometric Stereo Approach to Face Recognition”. But there are still opportunities; eigenfaces is the name given to a set of eigenvectors when they are used in the computer vision problem of human face recognition. Enact Robust Security Procedures to Minimize the Threat of Imposters on the Front End and Avoid Data Compromise on the Back End Because most biometrics cannot easily be changed, past examples of improper and unlawful police use of driver and vehicle data suggest face recognition data will also be misused. Space spanned by eigenfaces we have calculated and in that face, breast cancer diagnosis and prognosis via linear programming.

All faces on the current frame are detected in 0. 86 seconds depending on selected values for face roll and yaw tolerances, and face detection accuracy. After detection, a set of features is extracted from each face into a template in 0. Optionally, gender can be determined for each person on the image with predefined degree of accuracy during the template extraction. A conventional face identification system can be tricked by placing a photo in front of the camera. The liveness detection can be performed in passive mode, when the engine evaluates certain facial features, and in active mode, when the engine evaluates user’s response to perform actions like blinking or head movements.

Six basic emotions are analyzed: anger, disgust, fear, happiness, sadness and surprise. A confidence value for each of the basic emotions is returned for the face. Larger value for an emotion means that it seems to be more expressed in the face. The points can be optionally extracted as a set of their coordinates during face template extraction.

A quality threshold can be used during face enrollment to ensure that only the best quality face template will be stored into database. Head pitch can be up to 15 degrees in each direction from the frontal position. Head yaw can be up to 45 degrees in each direction from the frontal position. See technical specifications for more details.

Multiple samples of the same face. Biometric template record can contain multiple face samples belonging to the same person. These samples can be enrolled from different sources and at different times, thus allowing improvement in matching quality. For example a person might be enrolled with and without beard or mustache, etc. 0 face template matching algorithm can compare up to 40,000 faces per second on a PC. Also, 5 Kilobytes and 7 Kilobytes templates can be used to increase matching reliability.