Abstract: The present disclosure relates to a face identification apparatus and a method thereof. An apparatus includes: a processor configured to detect face information from image data, to extract face features from the detected face information based on a face feature extraction model weight-lightened through a weight-lightening technique, and to compare the face features with a previously stored face database to identify a face according to similarity; and a storage configured to store data and algorithms driven by the processor, and to have the face database.

Abstract: In an embodiment an apparatus includes a processor configured to generate a feature extraction module using a dataset in which an attribute for each object is defined, receive an image obtained by a camera, extract an attribute of an object of interest from the image using the learned feature extraction module, identify an object re-identification candidate group based on the extracted attribute of the object of interest and re-identify the object of interest based on the identified object re-identification candidate group.

Abstract: An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.

Abstract: An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.