We are offering face recognition system
some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features.[2] other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face detection. A probe image is then compared with the face data.[3] one of the earliest successful systems[4] is based on template matching techniques[5] applied to a set of salient facial features, providing a sort of compressed face representation.
Recognition algorithms can be divided into two main approaches, geometric, which looks at distinguishing features, or photometric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances.
Popular recognition algorithms include principal component analysis using eigenfaces, linear discriminate analysis, elastic bunch graph matching using the fisherface algorithm, the hidden markov model, and the neuronal motivated dynamic link matching.
with the recent days advancement in embedded technology, many embedded facial recognition products are available which though have limitation of number of users it can handle (because of memory limitation) gives performance almost similar to desktop counterparts.

We are offering face recognition system
some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features.[2] other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face detection. A probe image is then compared with the face data.[3] one of the earliest successful systems[4] is based on template matching techniques[5] applied to a set of salient facial features, providing a sort of compressed face representation.
Recognition algorithms can be divided into two main approaches, geometric, which looks at distinguishing features, or photometric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances.
Popular recognition algorithms include principal component analysis using eigenfaces, linear discriminate analysis, elastic bunch graph matching using the fisherface algorithm, the hidden markov model, and the neuronal motivated dynamic link matching.
with the recent days advancement in embedded technology, many embedded facial recognition products are available which though have limitation of number of users it can handle (because of memory limitation) gives performance almost similar to desktop counterparts.