Example systems and methods for defending against powerful, automated attacks on facial authentication systems are disclosed. A first verification is performed based at least in part on determining a response time for a response to a CAPTCHA or other challenge. In response to determining that the response time is within a threshold, a second verification is performed based at least in part on extracting a face feature or a voice feature from a plurality of samples associated with the response.

The present disclosure generally relates to digital identification credential user interfaces.

A computer-implemented method for identifying a user, the method using a computing device comprising an illumination source that, when activated, emits visible light, the method comprising taking two images of a scene potentially comprising a living body part carrying a biometric characteristic, wherein a first image is taken without the illumination source being activated and the second image is taken with the illumination source being activated, transferring the first image and the second image to a neural network and processing, by the neural network the first image and the second image, wherein the processing comprises comparing the first image and the second image, thereby determining whether the first image and the second image are images of a living body part, the method further comprising, if it is determined that the first image and the second image are images of a living body part, performing an identification algorithm to find a biometric characteristic for identifying the user and, if it is determined that the first image and the second image are not images of a living body part, not performing the identification algorithm.

Methods, systems, and computer-readable storage media for determining that a subject is a live person include capturing a set of images of a subject instructed to perform a facial expression. A region of interest for the facial expression is determined in a first image of the set, the first image representing a first facial state that includes the facial expression. A set of facial features is identified in the region of interest, the facial features being indicative of interaction between facial muscles and skin of the subject due to the subject performing the facial expression. A determination is made, based on the facial features, that the first image substantially matches a template image of the facial expression of the subject. Responsive to determining that the first image substantially matches the template image, identifying the subject as a live person.

Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

A sensing device includes a substrate, a first circuit, a second circuit, a first photodetector, and a second photodetector. The substrate has a sensing region. The first circuit is disposed on the substrate and in the sensing region, and configured to sense a fingerprint. The second circuit is disposed on the substrate and in the sensing region, and configured to sense a living body. The first photodetector is electrically connected to the first circuit. The second photodetector is electrically connected to the second circuit. The area of the second photodetector is larger than the area of the first photodetector.

A method with liveness testing may include: acquiring an infrared (IR) image including an object, and a depth image including the object; generating a first preprocessed IR image by performing first edge enhancement preprocessing on the IR image; generating a preprocessed depth image by performing second edge enhancement preprocessing on the depth image; and determining whether the object is a genuine object based on the first preprocessed IR image and the preprocessed depth image.

A liveness detection device comprising a light source unit, image sensor unit, and data processing module and authentication method thereof are provided. The light source unit comprises a substrate having a first inclined surface, whereby emitted light is reflected light from the first inclined surface. An application triggers an authentication process, which is indicated to a user. The light source unit begins illumination having a specific pattern and for a specific period and image signals are generated. Liveness detection signals are generated, via calculation of interference patterns, each, from more than one image signal, in sequence, for determination of liveness. When a liveness threshold is met, feature recognition data is generated, via calculation of interference patterns, each, from more than one image signal, in sequence, for matching. Then, the features are compared with previously enrolled data for locking or unlocking of the liveness detection device and/or system coupled thereto.

A processor-implemented vehicle controlling method includes: determining whether an object in a vehicle is a living object based on radio detection and ranging (radar) information received from a radar sensor; in response to a determination that the object is a living object, determining bioinformation of the object based on the radar information; and adjusting a temperature in the vehicle based on the bioinformation and temperature information received from a temperature sensor.

Biometric input, such as images of a hand obtained by a biometric input device, may be used to identify a person. An attacker may attempt to gain access by presenting false biometric data with an artificial biometric model, such as a fake hand. During a suspected attack, the attacker is prompted for additional data. For example, email address, telephone number, payment information, and so forth. This provides additional information about the attack while prolonging the time spent by the attacker on the attack. Information explicitly indicating failure is delayed or not presented at all. Data associated with an attack is placed into an exclusion list and further analyzed to recognize and mitigate future attacks. A subsequent attempt that corresponds to exclusion data proceeds with presenting prompts, gathering further information and consuming more of the attacker's time and resources.