Smile detection in real-life face images is an interesting problem with many potential applications. This paper presents an efficient approach to smile detection for face images captured in real-world unconstrained scenarios. In our approach, the pixel intensities in the gray-scale face image are compared, and the intensity differences are used as features. We adopt Adaboost to choose and combine intensity differences (based weak classifiers) to form a strong classifier for smile detection. With the simple features, the detection could be very fast. Our approach achieves 85% accuracy in smile detection by examining 20 pairs of pixel difference and 88% accuracy with 100 pairs of pixel comparison. We match the accuracy of Gabor features based SVM by examining as few as 350 pairs of pixel difference.