Abstract: An identity verification method performed at a terminal includes playing in an audio form action guide information including mouth shape guide information selected from a preset action guide information library at a speed corresponding to the action guide information, and collecting a corresponding set of action images within a preset time window; performing matching detection on the collected set of action images and the action guide information, to obtain a living body detection result indicating whether a living body exists in the collected set of action images; according to the living body detection result that indicates that a living body exists in the collected set of action images: collecting user identity information and performing verification according to the collected user identity information, to obtain a user identity information verification result; and determining the identity verification result according to the user identity information verification result.

Abstract: A method for identifying skin region in an image includes: selecting a pixel in the target image; calculating a first probability of the pixel in a first color space, the first probability being a probability that the pixel represents skin in the first color space; calculating a second probability of the pixel in a second color space distinct from the first color space, the second probability being a probability that the pixel represents skin in the second color space; determining a combined probability that the pixel in the target image represents skin based on the first probability and the second probability; expanding the pixel into a skin region in the target image by repeating said steps of determining a combined probability that another pixel adjacent to the pixel represents skin until the skin region reaches a predefined size threshold; and performing a predefined beautifying process to the pixels.

Abstract: The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.

Abstract: A sign-in method and server based on facial recognition are provided. The method includes: receiving a face image of a sign-in user from a sign-in terminal. According to the face image of the sign-in user, whether a target registration user matching the sign-in user exists in a pre-stored registration set is detected. The registration set includes a face image of at least one registration user. Further, the target registration user is confirmed as signed in successfully if the target registration user exists in the registration set.

Abstract: The present disclosure discloses a living body identification method, an information generation method, and a terminal, and belongs to the field of biometric feature recognition. The method includes: providing lip language prompt information, the lip language prompt information including at least two target characters, and the at least two target characters being at least one of: characters of a same lip shape, characters of opposite lip shapes, or characters whose lip shape similarity is in a preset range; collecting at least two frame pictures; detecting whether lip changes of a to-be-identified object in the at least two frame pictures meet a preset condition, when the to-be-identified object reads the at least two target characters; and determining that the to-be-identified object is a living body, if the preset condition is met.

Abstract: Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

Abstract: The present disclosure discloses a photo processing method and an apparatus for grouping photos into photo albums based on facial recognition results. The method includes: performing face detection on multiple photos, to obtain a face image feature set, each face image feature in the face image feature set corresponding to one of the multiple photos; determining a face-level similarity for each pair of face image features in the face image feature set; determining a photo-level similarity between each pair of photos in the multiple photos in accordance with their associated face-level similarities; generating a photo set for each target photo in the multiple photos, wherein any photo-level similarity between the target photo and another photo in the photo set exceeds a predefined photo-level threshold; and generating a label for each photo set using photographing location and photographing time information associated with the photos in the photo set.

Abstract: A method for identifying skin region in an image includes: selecting a pixel in the target image; calculating a first probability of the pixel in a first color space, the first probability being a probability that the pixel represents skin in the first color space; calculating a second probability of the pixel in a second color space distinct from the first color space, the second probability being a probability that the pixel represents skin in the second color space; determining a combined probability that the pixel in the target image represents skin based on the first probability and the second probability; expanding the pixel into a skin region in the target image by repeating said steps of determining a combined probability that another pixel adjacent to the pixel represents skin until the skin region reaches a predefined size threshold; and performing a predefined beautifying process to the pixels.

Abstract: An identity verification method performed at a terminal includes playing in an audio form action guide information including mouth shape guide information selected from a preset action guide information library at a speed corresponding to the action guide information, and collecting a corresponding set of action images within a preset time window; performing matching detection on the collected set of action images and the action guide information, to obtain a living body detection result indicating whether a living body exists in the collected set of action images; according to the living body detection result that indicates that a living body exists in the collected set of action images: collecting user identity information and performing verification according to the collected user identity information, to obtain a user identity information verification result; and determining the identity verification result according to the user identity information verification result.

Abstract: A method for identifying skin region in an image includes: obtaining a target image; and for each of a plurality of pixels in the target image, calculating a probability that the pixel corresponds to skin captured in the image, wherein calculating the probability includes: calculating a first probability of said each pixel in the target image in a first color space the first probability being a probability that the pixel is skin in the first color space; calculating a second probability of said each pixel in the target image in a second color space, the second probability being a probability that the pixel is skin in the second color space; and determining a combined probability that said each pixel in the target image is skin, the combined probability that the pixel is skin being an arithmetic mean value of the first probability and the second probability of the pixel.

Abstract: Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

Abstract: The method provided in the present disclosure includes: obtaining an image photographed by a camera, and performing face detection on the image by using a face detection algorithm, to obtain a face pixel set from the image; positioning a facial feature contour mask over the face pixel set, to obtain a to-be-examined pixel set from the face pixel set, the to-be-examined pixel set including: a plurality of pixels within an image area except pixels masked by the facial feature contour mask in the face pixel set; performing edge contour detection on the to-be-examined pixel set, and extracting one or more blemish regions from the to-be-examined pixel set, to obtain a to-be-retouched pixel set, the to-be-retouched pixel set including: a plurality of pixels within an image area belonging to the blemish regions; and retouching all pixels in the to-be-retouched pixel set, to obtain a retouched pixel set.

Abstract: An identity verification method performed at a terminal includes: displaying and/or playing in an audio form action guide information selected from a preset action guide information library, and collecting a corresponding set of action images within a preset time window; performing matching detection on the collected set of action images and the action guide information, to obtain a living body detection result indicating whether a living body exists in the collected set of action images; according to the living body detection result that indicates that a living body exists in the collected set of action images: collecting user identity information and performing verification according to the collected user identity information, to obtain a user identity information verification result; and determining the identity verification result according to the user identity information verification result.

Abstract: Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

Abstract: This application discloses a method and a terminal for detecting glasses in a face image. The method includes: obtaining a face image; determining a nose bridge region in the face image; detecting an image change in the nose bridge region to obtain an image change result of the nose bridge region; and determining whether there are glasses in the face image according to the image change result of the nose bridge region. The terminal for detecting glasses in a face image matches the method.

Abstract: The present disclosure discloses a photo processing method and an apparatus for grouping photos into photo albums based on facial recognition results. The method includes: performing face detection on multiple photos, to obtain a face image feature set, each face image feature in the face image feature set corresponding to one of the multiple photos; determining a face-level similarity for each pair of face image features in the face image feature set; determining a photo-level similarity between each pair of photos in the multiple photos in accordance with their associated face-level similarities; generating a photo set for each target photo in the multiple photos, wherein any photo-level similarity between the target photo and another photo in the photo set exceeds a predefined photo-level threshold; and generating a label for each photo set using photographing location and photographing time information associated with the photos in the photo set.

Abstract: Embodiments of the present invention provide a photograph processing method and system. The method includes: performing face detection on a photograph to obtain a detected human face; performing alignment on the detected human face, so as to obtain contour points of a left eye and a right eye of the detected human face; separately calculating a left eye area, being an area of the left eye, and a right eye area, being an area of the right eye, according to the contour points of the left eye and the right eye; performing stretching transformation on each pixel in the left eye area and the right eye area to generate a stretched left eye area and a stretched right eye area; and performing histogram equalization processing on the stretched left eye area and the stretched right eye area, so as to generate a processed photograph.

Abstract: The present disclosure discloses a photo processing method and an apparatus for grouping photos into photo albums based on facial recognition results. The method includes: performing face detection on multiple photos, to obtain a face image feature set, each face image feature in the face image feature set corresponding to one of the multiple photos; determining a face-level similarity for each pair of face image features in the face image feature set; determining a photo-level similarity between each pair of photos in the multiple photos in accordance with their associated face-level similarities; generating a photo set for each target photo in the multiple photos, wherein any photo-level similarity between the target photo and another photo in the photo set exceeds a predefined photo-level threshold; and generating a label for each photo set using photographing location and photographing time information associated with the photos in the photo set.

Abstract: A sign-in method and server based on facial recognition are provided. The method includes: receiving a face image of a sign-in user from a sign-in terminal. According to the face image of the sign-in user, whether a target registration user matching the sign-in user exists in a pre-stored registration set is detected. The registration set includes a face image of at least one registration user. Further, the target registration user is confirmed as signed in successfully if the target registration user exists in the registration set.

Abstract: The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.