Biometrics are increasingly used to provide authentication and/or verification of a user in many security and financial applications for example. However, “spoof attacks” through presentation of biometric artefacts that are “false” allow attackers to fool these biometric verification systems. Accordingly, it would be beneficial to further differentiate the acquired biometric characteristics into feature spaces relating to live and non-living biometrics to prevent non-living biometric credentials triggering biometric verification. The inventors have established a variety of “liveness” detection methodologies which can block either low complexity spoofs or more advanced spoofs. Such techniques may provide for monitoring of responses to challenges discretely or in combination with additional aspects such as the timing of user's responses, depth detection within acquired images, comparison of other images from other cameras with database data etc.

A system and method to facilitate trespass incident reporting. The system includes a camera, a processor, a communication device, a remote server, a storage device, and a user personal computing (PC) device. The processor aids in identifying a trespasser using the facial images acquired by the camera through a visual comparison with known trespassers. The processor sends the acquired facial images to the remote server through the communication device. Then the acquired facial images are matched with images of trespassers in a trespasser database stored on the storage device. When no match is found, the remote server prompts the user's PC device to enter information for the first-time trespasser, and to present an audio no-trespass warning message to the trespasser while concurrently recording the presentation using an audio/video recorder. With the no-trespass recording, a trespasser incident report is created and distributed to all users.

A face authentication apparatus includes a face image acquisition unit that acquires a face image of an authentication target, a collation unit that performs face authentication by calculating similarity between face information of the face image of the authentication target and reference face image of each registered user and comparing the similarity with a threshold, a prediction unit that predicts a change in the similarity on the basis of similarity history on authentication success, and a threshold change unit that changes the threshold on the basis of the prediction result.

A system and a method for glare prevention. The method includes providing an image view of the face of the vehicle occupant; determining levels of light at plural parts of the face of the vehicle occupant; determining a direction to a light source causing the levels of light at plural parts of the face of the vehicle occupant; determining an area where to activate a glare protector; and activating the glare protector in the determined area to protect the vehicle occupant from glare caused by a light source.

An image providing apparatus (10) includes a registration unit (12) for registering a plurality of captured images of a predetermined user taken at a plurality of locations, respectively, in a storage unit (11), an acquisition unit (13) for acquiring a first image of the user captured at a specific location, a specification unit (14) for specifying one or more second images including a face area whose degree of match with a face area of the user included in the first image is greater than or equal to a predetermined value from among the plurality of captured images, a generation unit (15) for generating a composite image including the specified second image, and an output unit (16) for outputting the composite image.

A facial structure estimating device 10 includes an acquiring unit 11 and a controller 13. The acquiring unit 11 acquires a facial image. The controller 13 functions as an identifier 15, an estimator 16, and an evaluator 17. The identifier 15 identifies an individual based on a facial image. The estimator 16 estimates a facial structure based on the facial image. The evaluator 17 calculates a validity of the facial structure estimated by the estimator 16. The evaluator 17 allows facial images and facial structures whose validity is greater than or equal to a threshold to be applied to training of the estimator 16. The controller 13 causes application of facial images and facial structures whose validity is greater than or equal to a threshold to training of the estimator 16 to be based on identification results of individuals produced by the identifier 15.

A mobile phone may include a display, wireless communication circuitry, a battery, and an enclosure enclosing the display, the wireless communication circuitry, and the battery. The enclosure may include a front cover formed from a transparent material and defining a front exterior surface of the mobile phone, a rear cover formed from a glass material and defining a rear exterior surface of the mobile phone, and a first housing component including a first wall section defining a first side exterior surface of the mobile phone, a second wall section defining a second side exterior surface opposite to the first side exterior surface, and a mid-chassis section extending between the first wall section and the second wall section.

Systems and methods for training a restoration model can leverage training for two sub-tasks to train the restoration model to generate realistic and identity-preserved outputs. The systems and methods can balance the training of the generation task and the reconstruction task to ensure the generated outputs preserve the identity of the original subject while generating realistic outputs. The systems and methods can further leverage a feature quantization model and skip connections to improve the model output and overall training.

Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.

The present invention discloses system and method for fake face identification. The system is a multi-spectral sensing based bio-security system. The system uses CNN module along with thermal sensors to detect human face and also detects the human temperature. The system authenticates a human face and in case of temperature generates an alarm as an alert.