Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We focus on automatically annotating faces in images based on the ambiguous supervision from the associated captions gives. Faces in the images are automatically detected using face detectors, and names in the captions are automatically extracted using a name entity detector. In existing system used LMNN (Large margin nearest neighbor). In existing system also used LRR (Low rank representation). In existing system developed a graph based method by constructing the similarity graph of faces. Drawbacks are Less Accuracy & Precision. In paper propose a new scheme for automatic face naming with caption-based supervision. We develop two methods Regularized low-rank representation (rLRR) and Ambiguously Supervised Structural Metric Learning (ASML). Two affinity matrices are further fused to generate one fused affinity matrix, based on which an iterative scheme is developed for automatic face naming.

Face detection, affinity matrix, human sensing, boosting, mat lab,supervised classification algorithm, etc.