An identification module is disclosed for use in a key making machine. The identification module may have a key receiving assembly configured to receive only a shank of an existing key. The identification module may also have a tip guide, configured to receive a tip of the shank of the existing key. The tip guide may have a slot that exposes a tip end of the shank. The identification module may also have an imaging assembly configured to capture an image of the tip end through the slot.

An electronic device for performing iris authentication, according to various examples of the present invention, can comprise: an image sensor for outputting an image obtained by photographing an eye part; a display for displaying an iris authentication screen image; and a control unit detecting at least a partial region from the captured eye part image so as to perform iris authentication by adjusting display characteristics of the display on the basis of a result obtained by comparing the size of the detected region with the size of a region required for the iris authentication, and various examples are possible.

An image recognition accelerator, a terminal device, and an image recognition method are provided. The image recognition accelerator includes a dimensionality-reduction processing module, an NVM, and an image matching module. The dimensionality-reduction processing module first reduces a dimensionality of first image data. The NVM writes, into a first storage area of the NVM according to a specified first current I, ω low-order bits of each numeric value of the first image data on which dimensionality reduction has been performed, and writes, into a second storage area of the NVM according to a specified second current, (N−ω) high-order bits of each numeric value of the first image data on which dimensionality reduction has been performed. The image matching module determines whether an image library stored in the NVM includes image data matching the first image data on which dimensionality reduction has been performed.

The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.

An automatic generation system of a training image and a method thereof are provided. The disclosure generates a training image and records the target category and the target position. The disclosure adds the target image to the container image as a candidate image, calculates a reliability of the candidate image, and repeatedly executes the process until the reliability of the candidate image meets a threshold condition for generating the training image. The disclosure is able to generate the training images automatically, and the recognition difficulty of the training image is adjustable by the user, so as to be suitable for customized recognition training.

A method and an apparatus include a display, an iris scanning sensor, and a processor functionally coupled with the display, and the iris scanning sensor, wherein the processor activates the iris scanning sensor when receiving a display-on event in a display-off state that is an intended user input.

A system, method and one or more wireless earpieces for authenticating a transaction utilizing one or more wireless earpieces. An indication that the transaction is pending is received by the one or more wireless earpieces. Biometric readings are performed for a user utilizing sensors of the one or more wireless earpieces. The biometric readings are analyzed to determine whether the user is authorized to perform the transaction. The transaction is authenticated in response to determining the biometric readings authorize the user to perform the transaction.

A model of a human subject's head may be generated to assist in a therapeutic and/or diagnostic procedure. A treatment and/or diagnostic system may generate a fitted head model using a predetermined head model and a plurality of points. The plurality of points may include facial feature information and may be determined using a sensor, for example, an IR or optical sensor. One or more anatomical landmarks may be determined and registered in association with the fitted head model using the facial feature information, for example, without the use of additional image information, such as an MM image. The fitted head model may include visual aids, for example, anatomical landmarks, reference points, marking of the human subject's MT location, and/or marking of the human subject's treatment location. The visual aids may assist a technician to perform the therapeutic and/or diagnostic procedure of the human subject.

A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

A machine learning model training method is applied to a first client, a plurality of clients are communicatively connected to a server, the server stores a plurality of modules, and the plurality of modules are configured to construct at least two machine learning models. The method includes: obtaining a first machine learning model, where at least one first machine learning model is selected based on a data feature of a first training data set stored in the first client; performing a training operation on the at least one first machine learning model by using the first data set, to obtain at least one trained first machine learning model; and sending at least one updated module to the server, where the updated module is used by the server to update weight parameters of the stored modules.