###### Abstract  
We propose a method of enlarging the training dataset for a single-sample-per-person (SSPP) face recognition problem. The appearance of the human face varies greatly, owing to various intrinsic and extrinsic factors. In order to build a face recognition system that can operate robustly in an uncontrolled, real environment, it is necessary for the algorithm to learn various images of the same person. However, owing to limitations in the collection of facial image data, only one sample can typically be obtained, causing difficulties in the performance and usability of the method. This paper proposes a method that analyzes the changes in pixels in face images associated with variations by extracting the binary weighted interpolation map (B-WIM) from neutral and variational images in the auxiliary set. Then, a new variational image for the query image is created by combining the given query (neutral) image and the variational image of the auxiliary set based on the B-WIM. As a result of performing facial recognition comparison experiments on SSPP training data for various facial-image databases, the proposed method shows superior performance compared with other methods. Keywords:image generation; weighted interpolation map; binarization; single sample per person +
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criminals usually attempt to disguise their identities when committing a crime [11; 12]. Even if they do not, it remains very difficult for systems to match active faces with the collected neutral images. As such, the dearth of learnable data restricts the use of feature-extraction and various other supervised methods [13; 14; 15; 16]. To solve the SSPP problem, several methods of enlarging training datasets have been proposed to generate new images from a given one. The theory of the evolution of technology suggests that such datasets can be expanded using existing means [17; 18; 19; 20]. In E(PC)\({}^{2}\)A2+ [21], extended from (PC)\({}^{2}\)A [22], an image and its corresponding half-, first-, and second-order projected images were used as the training set. In the (2D)\({}^{2}\)PCA method [23], new images were generated by simultaneously applying two-directional principal component analysis (PCA) [24] in the row and column directions of 2D images. In the SPCA+ method [25], the training set was enlarged by combining the original image linearly with its derived image obtained by perturbing the image matrix's singular values. In [26], concatenated left- and right-side images obtained from the symmetry of the face were used as training samples. In [27], images were generated using a symmetry transform for the intraclass and a linear combination for the interclass. In the interclass relationship (ICR) [28], data were generated by a weighted combination of (at least) two images in the training set. The ICR rectified the underestimated intraclass and overestimated the interclass. In MVI [29], the training set was enlarged by generating multiple low-resolution virtual images using a single high-resolution image. In SRGES [30], images were generated by adding mean images of the difference between neutral and variational images for each variation in the auxiliary set to the query image. In [31], occluded images were generated by using a weighted interpolation map and an auxiliary set. The weighted interpolation map represented the degrees of changes in pixels at the same positions between an image and its occluded version. The degree of changes was measured using the standard deviation of the difference between neutral and variational images in the auxiliary set. When generating a new image, the pixels at positions of large differences were replaced with the pixel values of the average image of the occluded images in the auxiliary set. In this paper, we propose binary weighted interpolation maps (B-WIM) to enlarge the training set for face recognition. Generally, the occurrence of variations leads the local pixels to change in the face image.