A system and method for video authentication may apply machine learning to analyze whether a person's face captured by live video matches a face in a photo ID captured by live video and to analyze other features based on a video session with the person. For example, machine learning may be applied to analyze a set of features indicating whether the person is a real, live person (as opposed to a photo image held up over the person's face in the video, etc.). Finally, the machine learning may be applied to analyze a set of features to determine whether a lower probability prediction that the person's face captured by live video matches a face in a photo ID captured by live video should be either pass authentication (due to one or more features/circumstances mitigating the lower probability) or fail authentication (due to one or more features not mitigating the lower probability). In such a situation, the set of features may indicate that mitigating factors/conditions exist that can offset the lower probability.

Provided in embodiments of the present application are a method and device for age estimation. The method comprises: performing gender training with respect to a gender model on the basis of facial image samples so as to allow the gender model to converge, where the gender model comprises at least two convolution layers; performing age training with respect to an age model on the basis of the facial samples so as to allow the age model to converge, where the age model comprises the at least two convolution layers, the converged age model comprises the weights of the at least two convolution layers, and the weights of the at least two convolution layers that the converged gender model comprises; and performing age estimation with respect to an inputted facial image on the basis of the converged age model. The technical solution provided in the embodiments of the present application eliminates the problem of inaccurate age estimation as a result of gender differences of facial images, thus increasing the accuracy of age estimation.

A pacification method based on emotion recognition, includes: acquiring at least one of a voice and an image of a user; determining whether the user has abnormal emotion, according to the at least one of the voice and the image of a user; and in response to the user having abnormal emotion, determining a pacification manner according to the emotion of the user, and performing emotional pacification on the user. An apparatus, a device and a storage medium are also provided.

Systems and methods employing unique optics, combined with data gathering techniques that capture user interaction in the system at the point of engagement and point of entry. This facilitates gathering real time data on users, customers or other people during engagement with elements of the system to provide analytics in real time.

In accordance with various embodiments, a self-service checkout terminal can comprise: a capture device having at least one sensor, wherein the capture device is configured: to capture first biometric data with reference to a person at the self-service checkout terminal; to capture second biometric data with reference to an official identity certificate if the identity certificate is presented to the capture device; to capture a product identifier of a product if the product is presented to the capture device; a control device configured for: firstly determining a sales restriction to which the product is subject, on the basis of the product identifier; comparing the first biometric data with the second biometric data; secondly determining whether the person satisfies a criterion of the sales restriction on the basis of a result of the comparing and on the basis of the second biometric data.

The invention relates to a machine learning model training method and device and electronic equipment, and relates to the technical field of artificial intelligence. The training method includes the following steps: inputting an image sample into a regression machine learning model, extracting a feature map of the image sample by utilizing the regression machine learning model, and determining an identification result of the image sample according to the feature map; inputting the feature map into a classification machine learning model, and determining the membership probability of the image sample belonging to each classification by using the classification machine learning model according to the feature map; calculating a first loss function according to the recognition result and the labeling result of the image sample, and calculating a second loss function according to the membership probability and the labeling result of the image sample; and training a regression machine learning model by using the first loss function and the second loss function.

Implementations of the present disclosure include receiving a color image and an IR image, the IR image taken contemporaneously with the color image, providing a set of facial landmarks depicted in the color image, determining a depth value for each facial landmark in the set of color landmarks, depth values being provided from the IR image, determining an average depth difference based on at least a sub-set of facial landmarks in the set of facial landmarks, comparing the average depth difference to a difference threshold to provide a comparison, and selectively authenticating the person based on the comparison.

A biological information acquisition device includes a detection-value acquisition unit to acquire a detection value from a non-contact biometric sensor, a vital measurement unit to measure a vital sign of a target person (TP) using the detection value, an image-data acquisition unit to acquire image data indicating an image captured by a camera, an image processing unit to perform at least one of a state estimation process of estimating a state of the target person, an attribute estimation process of estimating an attribute of the target person, or a personal identification process of identifying the target person by performing image processing on the image captured including the target person, and a parameter setting unit to set a parameter in measuring the vital sign in accordance with a result of the image processing.

A search starting point determination unit determines a search starting point in which there is a high presence probability of the target person at a first time based on the person information. A backward tracking unit estimates the presence probabilities of the target person at different locations at a second time before the first time based on the person information and determines an area where the presence probability is higher. A recognition unit calculates a degree of matching between the appearance information and the surveillance images in the area and determines a location at which the degree of matching is higher. A forward tracking unit tracks the candidates of the target person forward in time from the second time based on the person information and the location. A presence probability estimation unit estimates the presence probability of the target person at the first time.

An information processing apparatus includes: an acquisition unit that acquires height information relating to a height of a capturing position of an imaging device that captured an image in which a face of a user was detected; and a determination unit that determines, based on the height information, whether or not the user is a candidate for person requiring assistance, who may be a person requiring assistance.