Patent Application: US-201113149765-A

Abstract:
a method for automated categorization of human face images based on facial traits , said method comprising a facial trait extracting phase , comprising the steps of : providing a multitude of images comprising human faces , for each image sampling a multitude of points in said image to obtain point sample data , for each sampled point extracting visual features from said point sample data , for each image assigning said visual features to predefined codewords by applying a codebook transform , for each image extracting facial traits by applying a kernel - based learning method &# 39 ; s prediction algorithm to said codewords to establish the probability that a facial trait from a predefined set of facial traits is present in said image , and extract said facial trait for said image if said probability is higher than a predefined threshold .

Description:
the process first detects a face in an image and samples points on and around the detected face . then , every point is described by the same invariant visual feature set . the resulting point feature set is reduced with the help of a codebook transformation to a fixed - size codeword histogram . this histogram , together with face labels for training , forms the input for a kernel - based machine learning algorithm . finally , during testing , the process assigns facial trait probabilities to previously unlabeled face images . the process deliberately ignores detailed geometry and specific facial regions . the process exploits the same set of visual features , computed over the localized faces only , which allows us to categorize human face images based on any observable trait without the need for implementing specialized detectors . the process perceives categorization of faces in terms of observable traits as a combined computer vision and machine learning problem . given an n - dimensional visual feature vector x i , representing face image i , the aim is to obtain a measure , which indicates whether facial trait ω j is present in face i . the process may choose from various visual feature extraction methods to obtain x i , and from a variety of supervised machine learning approaches to learn the relation between ω j and x i . the supervised machine learning process is composed of two phases : training and testing . in the first phase , the optimal configuration of features is learned from the training data . in the second phase , the classifier assigns a probability p ( ω j | x i ) to each input feature vector for each face property . the process details the generic categorization scheme for facial traits by presenting a component - wise decomposition . the process follows the image data as they flow through the computational process , as summarized in the next paragraph , and detailed per component next . the process is able to categorize a human face image according to observable semantic features in a generic fashion . examples include gender , race , age , facial ( hair ) properties , abnormalities , and presence of religious , medical , or fashion elements such as hats , scarves , glasses , piercings , and tattoos . the process first detects and localizes a face in an image . then the face image is aligned to assure a canonical pose . from the canonical face pose , points are sampled on and around the face . then , every point is described by the same invariant visual feature set . the resulting point feature set is reduced with the help of a codebook transformation to a fixed - size codeword histogram . this histogram , together with face labels for training , forms the input for a kernel - based machine learning algorithm . the process then assigns facial trait probabilities to previously unlabeled face images , and labels the images accordingly . the process employs an off - the - shelf face detector for localizing faces in images . the process is suited for frontal faces or profile faces . once a face is detected and localized in the image , the process segments a bounding box around the detected face . this face image is transferred to the face alignment stage . the detected faces are not necessarily aligned into the same pose . however , since unaligned faces may introduce unwanted variability , it is well known that categorization performance benefits when faces are transferred into the same canonical pose . several methods for face alignment exist , e . g ., congealing , active appearance models , active wavelet networks , and so on . preferably an unsupervised technique is used which is able to align face images under complex backgrounds , lighting , and foreground appearance , as described in [ huan07 ]. the visual appearance of a observable trait in face images has a strong dependency on the spatio - temporal viewpoint under which it is recorded . salient point methods such as [ tuyt08 ] introduce robustness against viewpoint changes by selecting points , which can be recovered under different perspectives . another solution is to simply use many points , which is achieved by random or dense sampling . in order to determine salient points in the face , interest point detectors like harris - laplace rely on a harris corner detector . by applying it on multiple image scales , it is possible to select the characteristic scale of a local corner using the laplacian operator as described in [ tuyt08 ]. hence , for each visible corner in the face image , the harris - laplace detector selects a scale - invariant point if the local image structure under a laplacian operator has a stable maximum . for homogenous facial areas , like the cheeks , corners are often rare . hence , for these properties relying on an interest point detector can be suboptimal . to counter the shortcoming of interest points , random and dense sampling strategies have been proposed . the process employs dense sampling , which samples an image grid in a uniform fashion using a fixed pixel interval between regions . in our experiments the process uses an interval distance of 2 pixels and sample at multiple scales . both interest points and dense sampling give an equal weight to all keypoints , irrespective of their spatial location in the facial image . in order to overcome this limitation , the process incorporates the approach suggested by lazebnik et al . [ laze06 ] for scene categorization , and sample fixed subregions of a face image , e . g ., 1 × 1 , 2 × 2 , 4 × 4 , and so on . the process aggregates the different resolutions into a so called spatial pyramid , which allows for region - specific weighting . since every region is an image in itself , the spatial pyramid can be used in combination with both interest point detectors and dense point sampling . the process uses a spatial pyramid of 1 × 1 , 2 × 2 , 3 × 1 , 1 × 3 , 5 × 1 , and 1 × 5 regions in our experiments . varying the scale , viewpoint , lighting and other circumstantial conditions in the recording of a face will deliver different data , whereas the semantics has not changed . hence , the process needs visual features minimally affected by accidental recording circumstance , while still being able to distinguish faces with different semantics . some form of invariance is required , as described in [ smeu00 ], such that the feature is tolerant to the accidental visual transformations . to put it simply , an invariant visual feature is a computable visual property that is insensitive to changes in the content , for example caused by changing the illumination color , illumination intensity , rotation , scale , translation , or viewpoint . features become more robust when invariance increases , but they lose discriminatory power . hence , effective visual features strike a balance between invariance and discriminatory power . good features are local invariant descriptors such as the sift feature proposed by lowe [ lowe04 ], which describes the local shape of a region using edge orientation histograms . as sift relies on intensity only , many color variants have been proposed recently , which include opponentsift , csift , rgsift , and rgb - sift [ sand10 ]. opponentsift , for example , describes all the channels in the opponent color space using sift features . the information in the o 3 channel is equal to the intensity information , while the other channels describe the color information in the face image . the feature normalization , as effective in sift , cancels out any local changes in light intensity . another robust invariant local descriptor is surf [ bay08 ], which replaces the gradient with first order haar wavelet responses in x and y direction , exploits integral images for efficiency , and uses only 64 instead of 128 dimensions . in the preferred embodiment the process computes the opponentslft visual features around salient points obtained from the harris - laplace detector and dense sampling . for all visual features the process employs a set of spatial pyramids . to avoid using all visual features in an image , while incorporating translation invariance and a robustness to noise , the process follows the codebook approach , which is well known in object and scene categorization since 2001 [ leun01 , sivi03 ] but never used for categorizing face images according to observable visual traits . first , the process assigns visual features to discrete codewords predefined in a codebook . then , the process uses the frequency distribution of the codewords as a compact feature vector representing a face image . three important variables in the codebook representation are codebook construction , codeword assignment , and codebook size . an extensive comparison of codebook representation variables is presented by van gemert et al . [ geme10a , geme10b ]. choices include the quantization method used , such as k - means clustering , vocabulary trees [ moos08 ], and so on , the codeword assignment , e . g ., using a hard or soft variants , and the codebook size , ranging from a hundred to a million codewords . preferably the process employs codebook construction using k - means clustering in combination with hard codeword assignment and a maximum of 4 , 000 codewords . learning facial traits from codeword histograms is achieved by kernel - based learning methods . similar to the state - of - the - art , the process uses the support vector machine framework as described in [ vapn00 ] for supervised learning of facial traits . preferably the process uses the libsvm implementation as described in [ chan01 ] with probabilistic output . while the radial basis kernel function usually performs better than other kernels , it was recently shown by zhang et al . in [ zhan07 ] that in a codebook - approach the earth movers distance and χ 2 kernel are to be preferred . in general , the process obtains good parameter settings for a support vector machine , by using an iterative search on both c and kernel function k (•) on cross validation data . from all parameters q the process selects the combination that with the best average precision performance , yielding q *. the process measures performance of all parameter combinations and selects the combination with the best performance . the process uses a 3 - fold cross validation to prevent over - fitting of parameters . the result of the parameter search over q is the improved model p ( ω j | x i , q *), contracted to p *( ω j | x i ), which the process uses to fuse and to rank facial trait recognition results . it will be appreciated by those skilled in the art that changes can be made to the preferred embodiment described above without departing from the broad inventive concept thereof . it is understood , therefore , that this invention is not limited to the particular embodiments disclosed , but it is intended to cover modifications within the spirit and scope of the present invention as defined by the appended claims . the following publications are incorporated herein by reference as indicated in the above description : [ bay08 ] h . bay , a . ess , t . tuytelaars , l . van gool . surf : speeded up robust features . computer vision and image understanding , 110 ( 3 ): 346 - 359 , 2008 . 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