Abstract— Mammals have several characteristics that can be
distinguished, such as footprints, voice, and face shape. However,
some mammals have the same facial shape, so it is difficult to
distinguish. Recognition of mammal face shape can be done by
utilizing image processing and pattern recognition. To classify the
face shape of mammals, the Histogram of Oriented Gradients
(HOG) and Support Vector Machine (SVM) methods can be used.
The HOG method is used to retrieve the shape features of the

image, while SVM is used as a classifier. This study uses the LHI-
Animal-Faces dataset, which was taken as many as 15 species of

mammals; where for each type of mammal, 60 images were
selected, and the size was changed to 150x150 pixels. The image is
converted into a grayscale image for the HOG feature extraction
process. Furthermore, the classification process uses SVM. The
kernels used are Linear, Polynomial, and Gaussian kernels. The
testing process uses K-Fold Cross-Validation. The folds used are
3-fold, 4-fold, 5-fold, 6-fold, and 10-fold. The performance of the
HOG feature and the SVM method that gives the best results is the
Linear kernel using 10-fold with an accuracy value of 96.55%,
precision of 77.92%, and recall of 74.11%. The kernel sequence
that gives the best results in this test is the Linear kernel,
Polynomial kernel, and Gaussian kernel.