Abstract: The invention relates to a method of face detection and determination of face pose from a three-dimensional mesh comprising the steps of: determining (200) a curvature map of the three-dimensional surface, according to an average curvature or a Gaussian curvature, detecting (300), within the curvature map, a facial nose tip (B) where the three-dimensional facial surface has a predetermined curvature, determining (400) a face pose defined by coordinates of the nose tip (B) and by three angles of rotation of the face around three axes, delineating a face zone and comparison (450) between the delineated face zone and a reference face template, in order to validate the detection of a real face in the three-dimensional mesh. The invention also relates to a method for checking the identity of an individual, using a frontalized shot of their face obtained via the method for face detection and determination of pose applied to a not necessarily frontalized shot.

Abstract: The invention relates to a method of face detection and determination of face pose from a three-dimensional mesh comprising the steps of: determining (200) a curvature map of the three-dimensional surface, according to an average curvature or a Gaussian curvature, detecting (300), within the curvature map, a facial nose tip (B) where the three-dimensional facial surface has a predetermined curvature, determining (400) a face pose defined by coordinates of the nose tip (B) and by three angles of rotation of the face around three axes, delineating a face zone and comparison (450) between the delineated face zone and a reference face template, in order to validate the detection of a real face in the three-dimensional mesh. The invention also relates to a method for checking the identity of an individual, using a frontalized shot of their face obtained via the method for face detection and determination of pose applied to a not necessarily frontalized shot.

Abstract: The invention relates to a method for generating a three-dimensional facial model the shape of which can changed on the basis of a plurality of images of faces of persons, including the steps that involve: generating a facial template; acquiring shapes from examples of faces of persons; repeatedly changing the shape of the template for each example of a face of a person, so that the shape of the changed template corresponds to the shape of the face example, and determining the change in shape between the initial template and the changed template; and generating the facial model as a linear combination of the shape of the template and the changes in shape between the initial template and the changed template, for each example of a face of a person. The invention also relates to a method for processing an image of a face of a person such as to generate a three-dimensional image of the face of the person from of said deformable model.

Abstract: The invention relates to a method for generating a three-dimensional facial model the shape of which can changed on the basis of a plurality of images of faces of persons, including the steps that involve: generating a facial template; acquiring shapes from examples of faces of persons; repeatedly changing the shape of the template for each example of a face of a person, so that the shape of the changed template corresponds to the shape of the thee example, and determining the change in shape between the initial template and the changed template; and generating the facial model as a linear combination of the shape of the template and the changes in shape between the initial template and the changed template, for each example of a face of a person. The invention also relates to a method for processing an image of a face of a person such as to generate a three-dimensional image of the face of the person from of said deformable model.

Abstract: Disclosed is an improved algorithm for estimating the 3D shape of a 3-dimensional object, such as a human face, based on information retrieved from a single photograph by recovering parameters of a 3-dimensional model and methods and systems using the same. Beside the pixel intensity, the invention uses various image features in a multi-features fitting algorithm (MFF) that has a wider radius of convergence and a higher level of precision and provides thereby better results.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

Abstract: Disclosed is an improved algorithm for estimating the 3D shape of a 3-dimensional object, such as a human face, based on information retrieved from a single photograph by recovering parameters of a 3-dimensional model and methods and systems using the same. Beside the pixel intensity, the invention uses various image features in a multi-features fitting algorithm (MFF) that has a wider radius of convergence and a higher level of precision and provides thereby better results.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

Abstract: Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.