Face detection using locally linear embedding
Samuel Kadoury · eScholarship@McGill (McGill) · 2005
Human face detection in gray scale images has been researched extensively over the past decade, due to the recent emergence of applications such as security access control, visual surveillance and content-based information retrieval. However, this problem remains challenging because faces are non-rigid objects that have a high degree of variability in size, shape, color and texture. Indeed, few of the proposed face detection methods have been analyzed for performance under different conditions, such as head rotation, illumination, facial expression, occlusion and aging. Nowadays, most face detection methods are based upon statistical and learning strategies. Many of these appearance-based methods tend to increase data complexity, by mapping it onto a higher-dimensional space in order to extract the predominant features; this, however, often requires much more computational time. A novel technique that is gaining in popularity, known as Locally Linear Embedding (LLE), adopts a different approach to the problem by applying dimensionality-reduction to the data for learning and classification. Proposed by Roweis and Saul, the objective of this method is to determine a locally-linear fit, so that each data point can be represented by a linear combination of its closest neighbors. The first objective of the current research is to apply the LLE algorithm to 2D facial images, so as to obtain their representation in a sub-space under the unfavorable conditions stated above. The low-dimensional data then will be used to train a Support Vector Machine to classify images as being face or non-face. For this research, six different databases of cropped facial images, corresponding to variations in head rotation, illumination, facial expression, occlusion and aging, were built to train and test the classifiers. The second objective is to evaluate the feasibility of using the combined efficacy of the six SVM classifiers in a two-stage face detection approach. Experimental results obtained with image databases demonstrated that the performance of the proposed method was similar to and sometimes better than other face detection methods, introducing a viable and accurate alternative to previously existing techniques.