Methods, systems, and apparatuses, among other things, may perform persistent object tracking and reidentification through detection and continuous feature comparison. For example, video frames may be received (e.g., from a camera, an application, or a data storage device) and an object of interest may be detected at a first position in a video frame and the object of interest may be detected at a second position in another video frame. A track associated with the object of interest may be generated based on the detected first and second positions of the object of interest.

A data verification method is provided. Fingerprint information of N (N being an integer greater than 1) slices of to-be-sent data of a second device is received, the fingerprint information including first fingerprint information corresponding to an i.sup.th (i being an integer greater than 1) slice of the to-be-sent data and second fingerprint information corresponding to an (i1).sup.th slice of the to-be-sent data. The first fingerprint information is based on updating, by using the i.sup.th slice of the to-be-sent data, the second fingerprint information. An i.sup.th slice of data is received from the second device. The received second fingerprint information is updated by using the i.sup.th slice of data, to obtain third fingerprint information. Data verification failure indication information is transmitted to the second device in response to the third fingerprint information not matching the received first fingerprint information.

Type prediction method, apparatus and electronic device for recognizing an object in an image are disclosed. The method may include processing an image to be processed using a full image recognition technique to obtain a first type prediction result of an object in the image to be processed; processing a subject area of the image to be processed using a feature recognition technique to obtain a second type prediction result of an object of the subject area; determining whether the first type prediction result matches the second type prediction result; if the first type prediction result matches the second type prediction result, determining a type of the object of the image to be processed to be the first type prediction result or the second type prediction result.

A data verification method is provided. Fingerprint information of N (N being an integer greater than 1) slices of to-be-sent data of a second device is received, the fingerprint information including first fingerprint information corresponding to an i.sup.th (i being an integer greater than 1) slice of the to-be-sent data and second fingerprint information corresponding to an (i1).sup.th slice of the to-be-sent data. The first fingerprint information is based on updating, by using the i.sup.th slice of the to-be-sent data, the second fingerprint information. An i.sup.th slice of data is received from the second device. The received second fingerprint information is updated by using the i.sup.th slice of data, to obtain third fingerprint information. Data verification failure indication information is transmitted to the second device in response to the third fingerprint information not matching the received first fingerprint information.

An image recognizing method adopted by a platform is disclosed. The method first receives multiple targets to be recognized at the platform, and inquiries a pre-established semantic tree by reference to the targets for determining if the recognition results of the multiple targets will cause confusion or not. If confusion is not foreseeable, the method obtains respectively a parent-classifier corresponding to each parent-category of each of the targets, and uses the parent-classifiers directly to perform a recognition action to the targets. Otherwise, the method obtains respectively multiple child-classifiers corresponding to multiple subcategories below each of the targets, and uses the multiple child-classifiers to perform such recognition action to the targets.

Automated facial recognition is performed by operation of a convolutional neural network including groups of layers in which the first, second, and third groups include a convolution layer, a max-pooling layer, and a parametric rectified linear unit activation function layer. A fourth group of layers includes a convolution layer and a parametric rectified linear unit activation function layer.

Type prediction method, apparatus and electronic device for recognizing an object in an image are disclosed. The method may include processing an image to be processed using a full image recognition technique to obtain a first type prediction result of an object in the image to be processed; processing a subject area of the image to be processed using a feature recognition technique to obtain a second type prediction result of an object of the subject area; determining whether the first type prediction result matches the second type prediction result; if the first type prediction result matches the second type prediction result, determining a type of the object of the image to be processed to be the first type prediction result or the second type prediction result.

The present disclosure relates to detecting the location of a face feature point using an Adaboost learning algorithm. According to some embodiments, a method for detecting a location of a face feature point comprises: (a) a step of classifying a sub-window image into a first recommended feature point candidate image and a first non-recommended feature point candidate image using first feature patterns selected by an Adaboost learning algorithm, and generating first feature point candidate location information on the first recommended feature point candidate image; and (b) a step of re-classifying said sub-window image classified into said first non-recommended feature point candidate image, into a second recommended feature point candidate image and a second non-recommended feature point candidate image using second feature patterns selected by the Adaboost learning algorithm, and generating second feature point candidate location information on the second recommended feature point recommended candidate image.