Face detection and face recognition are two techniques in the field of image processing which have undergone significant research during the past few years. It stems from the availability of powerful algorithms, hardware and the wide range of applications they have got. Though powerful hardware and algorithms are available, today's face detection systems are far from perfect since they work within certain constraints. The performance of a face detection system is affected by the factors such as illumination, pose, occlusion etc. An efficient algorithm is the one which takes into account all the above factors, which would in turn increase the time complexity. Since time complexity acts as the bottleneck, development of an algorithm which detects face in minimum time is the need of the hour. Computations will take less time if a sparse representation can be provided for the image. Curvelet transform is an analysis tool which has the ability to sparsely represent images with curve discontinuities. In this work, curvelet transform is studied and is used to represent face images. Principal component analysis is done on this representation to reduce the dimension of the data. Euclidean distance is the parameter used to classify the face from non-faces. The performance of the system is analyzed using receiver operating characteristics (ROC)
