A mold temperature control system includes: an inference model generating portion configured to, based on a plurality of pieces of thermo image data of a mold acquired at predetermined intervals and pieces of teaching data associated with the plurality of pieces of thermo image data, learn a predetermined number of consecutive pieces of time-series image data extracted from the plurality of pieces of thermo image data as one piece of sample data to generate an inference model for detecting a sign of a temperature anomaly of the mold; a mold temperature anomaly degree inference portion configured to detect occurrence of the sign of the temperature anomaly of the mold the predetermined number ahead, using the inference model, based on the predetermined number of pieces of time-series image data of the mold; and a warning lamp request transmitting portion configured to control lighting-up of a warning lamp.

The present disclosure provides a method for facilitating implementing biometric recognition. Further, the method may include receiving two or more biometric images of one or more biometric identifiers of one or more individuals from one or more devices. Further, the two or more biometric images may be in two or more spectrums. Further, the method may include analyzing the two or more biometric images using one or more deep hashing network models. Further, the method may include extracting two or more discriminative deep hashing codes from the two or more biometric images based on the analyzing. Further, the method may include generating a biometric template based on the two or more discriminative deep hashing codes. Further, the method may include generating a biometric key for the one or more biometric identifiers using a fuzzy commitment scheme based on the biometric template. Further, the method may include storing the biometric key.

The present disclosure provides a method for facilitating implementing biometric recognition. Further, the method may include receiving two or more biometric images of one or more biometric identifiers of one or more individuals from one or more devices. Further, the two or more biometric images may be in two or more spectrums. Further, the method may include analyzing the two or more biometric images using one or more deep hashing network models. Further, the method may include extracting two or more discriminative deep hashing codes from the two or more biometric images based on the analyzing. Further, the method may include generating a biometric template based on the two or more discriminative deep hashing codes. Further, the method may include generating a biometric key for the one or more biometric identifiers using a fuzzy commitment scheme based on the biometric template. Further, the method may include storing the biometric key.

A detection and communication module arranged to implement a face detection function and an optical wireless communication function, and including a processing unit, a transmission chain and a reception chain, the processing unit being arranged to transmit via the transmission chain a detection signal, to receive via the reception chain the detection signal following its reflection on a face surface of an individual, and to evaluate a distance between the detection and communication module and the face surface of the individual, the processing unit being further arranged to transmit via the transmission chain a transmitted optical wireless communication signal containing data to be transmitted, and to receive via the reception chain a received optical wireless communication signal.

A detection and communication module arranged to implement a face detection function and an optical wireless communication function, and including a processing unit, a transmission chain and a reception chain, the processing unit being arranged to transmit via the transmission chain a detection signal, to receive via the reception chain the detection signal following its reflection on a face surface of an individual, and to evaluate a distance between the detection and communication module and the face surface of the individual, the processing unit being further arranged to transmit via the transmission chain a transmitted optical wireless communication signal containing data to be transmitted, and to receive via the reception chain a received optical wireless communication signal.

This disclosure relates to a system and a method for automated recognition of drugs. This disclosure also relates to a system for automated recognition of drugs comprising a hyper-spectral imaging system. This disclosure also relates to a hyper-spectral imaging system configured to automatically recognize drugs by using a neural network. This disclosure relates to training the neural network to identify a drug type (e.g., the name of the drug) based on an image (e.g., normal visible image and/or hyperspectral image) of the drug.

An information processing apparatus includes: a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions to: obtain a first image including a first representation of an iris region of a first eye; obtain a second image including a second representation of an iris region of a second eye; receive an input to rotate at least one of the first image or the second image; and control a display to display the at least one of the first image or the second image in a rotated state based on the input.

Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

Methods, systems, and programming for user identification are presented. In one example, a system for acquiring biometric information is disclosed. The system comprises a housing including a surface for a person to place a finger thereon. The system also comprises a sensor, a first image acquisition portion, and a second image acquisition portion. The sensor is configured for sensing presence of the finger when the person places the finger on the surface. The first image acquisition portion is configured for acquiring a fingerprint image of the finger placed on the surface. The second image acquisition portion is configured for acquiring a finger vein image of the finger placed on the surface. The first and second image acquisition portions acquire their respective images at different times.

Methods, systems, and programming for user identification are presented. In one example, a system for acquiring biometric information is disclosed. The system comprises a housing including a surface for a person to place a finger thereon. The system also comprises a sensor, a first image acquisition portion, and a second image acquisition portion. The sensor is configured for sensing presence of the finger when the person places the finger on the surface. The first image acquisition portion is configured for acquiring a fingerprint image of the finger placed on the surface. The second image acquisition portion is configured for acquiring a finger vein image of the finger placed on the surface. The first and second image acquisition portions acquire their respective images at different times.