Methods, systems, and computer-readable storage media for determining that a subject is a live person using a color-coded sequence including a sequence of colors. A subject is illuminated in accordance with the sequence of colors. A sequence of images of the subject is captured, where the sequence of images are temporally synchronized with illumination by the color-coded sequence. A filtered response image is generated, by a matched filtering process on the sequence of images using the selected color-coded sequence. A determination is made, based on structural features around an eye region of the filtered response image, that the subject is a live person. Responsive to determining that the subject is a live person, initiating an authentication process to authenticate the subject.

The present disclosure provides a neural network model training method and apparatus, a living body detecting method and apparatus, a device and a storage medium. It is feasible to, with respect to different scenarios, respectively obtain picture samples under the scenarios, each picture sample having a tag, the tag including a living body or a non-living body and a corresponding scenario; and according to obtained picture samples, train to obtain a neural network model whose output is a feature vector which is discriminative for different scenarios. As such, when the living body detection is performed, it is possible to determine the feature vector of the to-be-detected picture according to the neural network model, then determine a picture sample matched with the to-be-detected picture, and regard the tag of the matched picture sample as the tag of the to-be-detected picture, thereby obtaining the detection result of the to-be-detected picture.

A method, an electronic device, and a storage medium are disclosed. The method includes: acquiring a color sequence verification code; controlling a screen of an electronic device to sequentially generate colors based on a sequence of the colors included in the color sequence verification code; controlling a camera of the electronic device to collect an image of a face of a target object in each of the colors to acquire an image sequence; performing a face liveness verification on the target object to acquire a liveness score value; acquiring difference images corresponding respectively to the colors of the images of the image sequence based on the image sequence; performing a color verification based on the color sequence verification code and the difference images; and determining a face liveness detection result of the target object based on

Technologies are provided for assessing liveness of a subject presented to a set of one or more cameras. A first image and a second image depicting a face are captured during a first time period, and a third image and a fourth image depicting the face are captured during a second time period. A first variance in facial appearance is detected between the first depiction of the face and the third depiction of the face, and a second variance in facial appearance is detected between the second depiction of the face and the fourth depiction of the face. A liveliness of a person is verified based on a determination that the first variance in facial appearance corresponds in time to the second variance in facial appearance.

A presentation attack detection system includes RGB and thermal imaging cameras for acquiring images of an individual and a processor operable to determine a metric for whether the individual is a real human based on the thermal imaging and RGB image data. Related methods are also described.

A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.

A face anti-counterfeiting detection method includes: obtaining an image or video to be detected containing a face; extracting a feature of the image or video to be detected, and detecting whether the extracted feature contains counterfeited face clue information; and determining whether the face passes the face anti-counterfeiting detection according to a detection result.

A liveness detection method is provided. In the method, image features are extracted from an image of a user. Convolution processing is performed on the image features through an estimation network to obtain a predicted mean value and a predicted variance of the image features. Standardization processing is performed on the image features based on the predicted mean value, the predicted variance, and predetermined network parameters of the standardization processing to obtain standardized features. Whether the image of the user includes a living body image is determined according to a liveness classification probability of a classification performed on the image of the user based on the standardized features. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

The present invention discloses a liveliness detection technique. The technique is described for identifying facial attributes. The technique identifies the presented face in the image as real or deceptive. The system and method includes identifying the facial attributes and utilizing a multi task learning network. The neural network includes segmentation and classification functionalities. The final output is used to get pixel level semantic information and high level semantic information.

Methods, systems, and computer-readable storage media for determining that a subject is a live person include obtaining, by an image capture device, a set of subject images. Each image is captured at a different corresponding relative location of the image capture device with respect to the subject. Parameters are determined from the set of images of the subject. The parameters represent corneal reflections of at least one object in at least one eye of the subject. A determination is made, based on the parameters, that the subject is a live person. Responsive to determining that the subject is a live person, an authentication process is initiated to authenticate the subject.