Abstract: A face authentication or recognition system embodiment includes a processing unit; a flash illumination drive circuit; a flash illumination unit having a flashlamp configured to generate a set of flash illumination pulses; a set of spectral filters configured to pass a set of spectrally filtered flash illumination pulses; a lens; an image sensor configured to receive a set of filtered flash illumination pulses reflected from a subject's face and generate a corresponding facial image dataset; and a memory or data storage device configured to store facial image datasets, enrollment datasets, and query datasets, and which includes a face authentication or recognition module. Spectrally filtered flash illumination pulses have an intensity at least approximately equal to the intensity of ambient sunlight, essentially regardless of an outdoor environment under consideration upon or proximate to the surface of the earth.

Abstract: A face authentication or recognition system embodiment includes a processing unit; a flash illumination drive circuit; a flash illumination unit having a flashlamp configured to generate a set of flash illumination pulses; a set of spectral filters configured to pass a set of spectrally filtered flash illumination pulses; a lens; an image sensor configured to receive a set of filtered flash illumination pulses reflected from a subject's face and generate a corresponding facial image dataset; and a memory or data storage device configured to store facial image datasets, enrolment datasets, and query datasets, and which includes a face authentication or recognition module. Spectrally filtered flash illumination pulses have an intensity at least approximately equal to the intensity of ambient sunlight, essentially regardless of an outdoor environment under consideration upon or proximate to the surface of the earth.

Abstract: In the last few years, face expression measurement has been receiving significant attention mainly due to advancements in areas such as face detection, face tracking and face recognition. For face recognition systems, detecting the locations in two-dimension (2D) images where faces are present is a first step to be performed before face expressions can be measured. However, face detection from a 2D image is a challenging task because of variability in imaging conditions, image orientation, pose, presence/absence of facial artefacts facial expression and occlusion. Existing efforts to address the shortcomings of existing face recognition systems deal with technologies for creation of three-dimensional (3D) models of a human subject's face based on a digital photograph of the human subject. However, such technologies are computationally intensive nature and susceptible to errors and hence might not be suitable for deployment.

Abstract: A method for replacing an object in an image is disclosed. The method comprises obtaining a first image having a first object. The first image is two-dimensional while the first object has feature portions. The method also comprises generating first image reference points on the first object and extracting object properties of the first object from the first image. The method further comprises providing a three-dimensional model being representative of a second image object and at least one of manipulating and displacing the three-dimensional model based on object properties of the first object. The method yet further comprises capturing a synthesized image containing a synthesized object from the at least one of manipulated and displaced three-dimensional model, the synthesized object having second image reference points and registering the second image reference points to the first image reference points for subsequent replacement of the first object with the synthesized object.

Abstract: With the ubiquity of new information technology and media, face and facial expression recognition technologies have been receiving significant attention. For face recognition systems, detecting the locations in two-dimension (2D) images where faces are present is a first step to be performed. However, face detection from a 2D image is a challenging task because of variability in imaging conditions, image orientation, pose, presence/absence of facial artefacts facial expression and occlusion. Existing efforts to address the shortcomings of existing face recognition systems involve technologies for creation of three-dimensional (3D) models of a human subject's face based on a digital photograph of the human subject. However, such technologies are computationally intensive nature and susceptible to errors and hence might not be suitable for deployment.

Abstract: In the last few years, face expression measurement has been receiving significant attention mainly due to advancements in areas such as face detection, face tracking and face recognition. For face recognition systems, detecting the locations in two-dimension (2D) images where faces are present is a first step to be performed before face expressions can be measured. However, face detection from a 2D image is a challenging task because of variability in imaging conditions, image orientation, pose, presence/absence of facial artefacts facial expression and occlusion. Existing efforts to address the shortcomings of existing face recognition systems deal with technologies for creation of three-dimensional (3D) models of a human subject's face based on a digital photograph of the human subject. However, such technologies are computationally intensive nature and susceptible to errors and hence might not be suitable for deployment.

Abstract: A single image from a camera (14) is captured of an individual (40) seeking entry through a door held by a door latch (24). An image processor (16) looks for and locates a tag (42) worn by the individual (40) in the image and reads an identification (ID) code from the tag (42). A comparator (20) compares this ID code with ID codes in an identification database (22) to find a match. Once a match of ID codes is found, the image processor (16) looks for and locates a face (44) of the individual (40) in the image and extracts facial features from the face (44). The comparator (20) compares the extracted facial features with facial features associated with the matched ID code, from the identification database (22), to find a match. Once there is a match of facial features, the door latch (24) is released.