Finding Faces in Gray Scale Images Using Locally Linear Embeddings

  • Samuel Kadoury
  • Martin D. Levine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


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 classi-fication 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 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, 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.


Facial Image Support Vector Regression Support Vector Machine Classifier Face Detection Head Rotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Samuel Kadoury
    • 1
  • Martin D. Levine
    • 1
  1. 1.Department of Electrical and Computer EngineeringMcGill UniversityMontrealCanada

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