Arcsoft Corporation Limited

Arsoft, Inc.

Shu Li

Jin Wang

Jiangen Cao

Han Qiu

Guogang Wang

Huaqi Zhang

The present invention discloses a face tracking method for electronic camera devices. The method is applied to an electronic camera device having a face database and a face classifier, and the face database is provided for storing data such as a position, a size and a skin color prototype of a face in a previously stored preview image, and the method includes the steps of: obtaining a current preview image; determining whether or not a known face exists in the face database; defining a searching space on the current preview image; and using the face classifier to detect the searching space in the current preview image, and determining whether or not a face exists in the searching space.

Face tracking method for electronic camera device

A method for detecting a facial expression and repairing a smile face of a portrait photo includes the steps of: detecting a location and a range of a mouth region in an inputted portrait photo; capturing a patch in the mouth region and a predetermined peripheral range thereof; executing a comparison process to a smile state or a stiff state of the mouth region in the patch by a mouth state classifier; executing a calculation process to a repaired region of the mouth region when the mouth region is determined to be in the stiff state, in order to calculate a location of a plurality of feature points in the repaired region of the mouth region; and executing an image warping process to the location of the feature points and adjacent pixels thereof, for generating a portrait photo showing a smile state.

Method for detecting facial expression and repairing smile face of portrait photo

The present invention is to provide a method of restoring closed-eye portrait photo, which comprises the steps of detecting the locations and range of the eyes of a portrait photo being taken, retrieving a patch of a designated range of the eyes area and its neighboring area which represents the expression of the eyes and its neighboring area, determining whether the eyes of said patch are open or closed by using an eyes state classifier, temporarily storing said patch as an open-eye templet when it is determined that the eyes are open, detecting the locations and range of the eyes of a subsequently taken portrait photo, calculating an eyes restoration area when it is determined that the eyes are closed, replacing the closed-eye patch with said open-eye templet, and performing fusion operation toward said eyes restoration area to ensure that each portrait photo generated is with the eyes opened.

Method of restoring closed-eye portrait photo

A method for detecting a facial area on a color image includes (a) placing a search window on the color image, (b) determining if a center pixel of the search window is a skin color pixel, indicating that the search window is a possible facial area candidate, (c) applying a 3-rectangle filter to the search window to determine if the search window is a possible facial area candidate, (d) applying a 4-rectangle filter to the search window to determine if the search window is a possible facial area candidate, (e) if steps (b), (c), (d) all determine that the search window is a possible facial area candidate, applying an AdaBoost filter to the search window to determine if the search window is a facial area candidate, and (f) if step (e) determines that the search window is a facial area candidate, saving the location of the search window.

Face detection on mobile devices

In a method for detecting facial expressions of a portrait photo by an image capturing electronic device, a face captured in the portrait photo is detected. The position and range of the opened and closed facial features are detected, and the facial features within an identified range are magnified according to a specific proportion. A patch of facial features and their surroundings within a specific range is cut according to the magnified identified range, so that the patch can show a change of facial expressions and a specific range of their surroundings. A facial feature classifier is trained by a specific number of opened and closed facial feature samples based on the Adaboost algorithm and used for detecting the facial features in the patch to determine whether the facial feature is situated at an opened state or a closed state.

Method for detecting facial expressions of a portrait photo by an image capturing electronic device

A known face guided imaging method applied for an electronic imaging device uses the scale of a known face sensed in a previous frame to define a scale range of a searching window, which adds a scale constraint into the searching window so as to search a current frame horizontally and vertically in the searching window to perform a face searching loop for a face. The invention simply sends a corresponding image patch within the scale range to a face detector for the face detection, and the searching space is constrained in a small group of scale ranges without the need of detecting the whole image of various different scales within the scale range, and thus the invention effectively reduces the huge quantity of computing values required for the detection process and greatly enhances the speed and efficiency of the face detection.

Known face guided imaging method

Locating an eye includes generating an intensity response map by applying a 3-rectangle filter and applying K-mean clustering to the map to determine the eye. Locating an eye corner includes applying logarithm transform and grayscale stretching to generate a grayscale eye patch, generating a binary map of the patch by using a threshold based on a histogram of the patch, and estimating the eye corner by averaging coordinates weighted by minimal eigenvalues of spatial gradient matrices in a search region based on the binary map. Locating a mouth corner includes generating another intensity response map and generating another binary map using another threshold based on another histogram of the intensity response map. Locating a chin or a cheek includes applying angle constrained gradient analysis to reject locations that cannot be the chin or cheek. Locating a cheek further includes removing falsely detected cheeks by parabola fitting curves through the cheeks.

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Hangzhou, China

Shu Li