Disclosed are a mobile terminal capable of implementing an always-on function with a lower power, based on a motion, and a motion-based low power implementing method thereof. The method includes sensing a motion of a user by using an acceleration sensor in a low-power always-on that only the acceleration sensor is activated; a selectively activating an always-on function according to the sensed motion and deactivating the always-on function when no motion is sensed in an always-on state and automatically entering to the low-power always-on function.

An object detection model generation method as well as an electronic device and a computer readable storage medium using the same are provided. The method includes: during the iterative training of the to-be-trained object detection model, the detection accuracy of the iteration nodes of the object detection model is sequentially determined according to the node order, and the mis-detected negative samples of the object detection model at the iteration nodes with the detection accuracy less than or equal to a preset threshold are enhanced. Then the object detection model is trained at the iteration node based on the enhanced negative samples and a first amount of preset training samples. After the training at the iteration nodes are completed, it returns to the step of sequentially determining the detection accuracy of the iteration nodes of the object detection model until the training of the object detection model is completed.

A method of processing an image involves detecting a face from a first image; determining a composition of the first image; selecting a composition of a second image according to the composition of the first image; and generating the second image including the face by trimming the first image, according to the composition of the second image.

A system for evenly-scattered adaptable subject lighting for mobile device photography, comprising a case configured to enclose and securely fasten a mobile device and an illuminating portion, the illuminating portion configured to produce light based on input received from a plurality of sensors. The system further comprising a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device, and configured to direct the operation of the illuminating portion comprising at least a sensor processor and an image capture device, wherein the sensor processor adjusts the illuminating portion based on preconfigured thresholds to provide evenly-scatter lighting for capturing images of subjects via the image capture device.

An appropriate skin color range can be promptly set. An image processing apparatus configured to estimate a pulse of a person detected from an image includes a setting unit configured to set an ellipse region including a face detected from the image, an acquisition unit configured to acquire color information about a pixel included in the set ellipse region, a determination unit configured to determine whether the acquired color information satisfies a predetermined condition, and a specifying unit configured to specify a threshold value indicating a skin color range based on the color information in the ellipse region in a case where the determination unit determines that the acquired color information satisfies the predetermined condition.

The technology disclosed herein provides a method of providing face-based tone curve adjustment to improve global and local contrast and brightness enhancement. In one implementation, the method includes determining a light level of an image captured by a camera, determining a contrast level of an image captured by a camera, comparing the light level and the contrast level to one or more of a plurality of thresholds, and based on the result of the comparison, selecting an advance intelligent global brightness contrast enhancement (AIGBCE) curve to the image.

An image lighting method includes: determining a convolutional neural network corresponding to a lighting operation type of an initial image; obtaining local feature information and global feature information of initial image according to convolutional neural network; obtaining fusion feature information of initial image according to convolutional neural network based on local feature information and global feature information; obtaining a maximum pooling result map and a minimum pooling result map according to convolutional neural network based on a luminance component map of initial image; obtaining a bilateral grid matrix of initial image based on fusion feature information, maximum pooling result map, and minimum pooling result map; and performing affine transformation on initial image according to bilateral grid matrix to obtain a target image, the target image being an image obtained after lighting initial image according to lighting operation type.

The detection device 1X mainly includes an acquisition means 12X, a common feature quantity extraction means 13X, a feature information detection means 14X, an attribute identification means 15X, and an output means 16X. The acquisition means 12X acquires data relating to a detection target. The common feature quantity extraction means 13X extracts, from the data, common feature quantity common to plural candidates of an attribute of the detection target. The feature information detection means 14X detects the feature information for each of the plural candidates based on the common feature quantity. The attribute identification means 15X identifies the attribute of the detection target based on the data. The output means 16X outputs the feature information corresponding to the identified attribute.

An apparatus for recognizing faces with different illuminations may include a processor and memory storing executable computer program code causing the apparatus to at least perform operations including detecting and extracting face data of a first candidate face of a first image and a second candidate face of a second image. The first image is associated with a first light intensity and the second image associated with a second light intensity different from the first light intensity. The computer program code may further cause the apparatus to analyze face data to determine whether the first candidate face corresponds to an area in the first image that is substantially the same as an area of the second candidate face and evaluate data of the first and second areas to determine whether the first and second candidate faces are valid or invalid faces. Corresponding methods and computer program products are also provided.

The present invention discloses a system integrating video communication and physical sign analysis, comprising at least one front-end device. The front-end device comprises a camera device, a display device, an audio device, a button device and a processor. The camera device, the display device, the audio device and the button device are all connected to the processor, and the processor can connect to the Internet network and the mobile device via wired or wireless means. The front-end device and the mobile device can perform video communication, and the front-end device can perform physical sign analysis according to the images collected by the camera device. Based on some applications in the prior art, the present invention combines video collection technology and human face analysis technology to perform physical sign analysis and obtain related indexes.