Finding Faces in Gray Scale Images Using Locally Linear Embeddings

The problem of face detection remains challenging because faces are non-rigid objects that have a high degree of variability with respect to head rotation, illumination, facial expression, occlusion, and aging. A novel technique that is gaining in popularity, known as Locally Linear Embedding (LLE), performs dimensionality reduction on data for learning and classification purposes. This paper presents a novel approach to the face detection problem by applying the LLE algorithm to 2D facial images to obtain their representation in a subspace under the specific conditions stated above. The low-dimensional data are then used to train Support Vector Machine (SVM) classifiers to label windows in images as being either face or non-face. Six different databases of cropped facial images, corresponding to variations in head rotation, illumination, facial expression, occlusion and aging, were used to train and test the classifiers. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other face detection methods, while using a lower amount of training images, thus indicating a viable and accurate technique.