Abstract— Smile detection in face images captured in unconstrained real-world scenarios is an interesting problem with many potential applications. This paper presents an efficient approach to smile detection,< Final Year Projects > in which the intensity differences between pixels in the grayscale face images are used as features. We adopt AdaBoost to choose and combine weak classifiers based on intensity differences to form a strong classifier. Experiments show that our approach has similar accuracy to the state-of-the-art method but is significantly faster. Our approach provides 85% accuracy by examining 20 pairs of pixels and 88% accuracy with 100 pairs of pixels. We match the accuracy of the Gabor-feature-based support vector machine using as few as 350 pairs of pixels.