Face detection 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. This paper employs a novel technique, known as locally linear embedding (LLE), for solving the face detection problem. The LLE method performs dimensionality reduction on data for learning and classification purposes. Proposed by Roweis and Saul, the intent of LLE 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 this research is to apply the LLE algorithm to 2D facial images to obtain their representation in a sub-space 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, correspond...

• Type: Journal
• Year: 2007
• Where: CVIU
• Authors: Samuel Kadoury, Martin D. Levine
• Keywords: CVIU 2007, Face Detection, Face Detection Problem, Facial Expression