In this paper, we propose a novel approach to automatically index digital home photos based on person identity. A person is identified by his/her face and clothes. The proposed method consists of two parts: clustering and indexing. In the clustering, a series of unlabeled photos is aligned in taken-time order, and is divided into several sub-groups by situation. The situation groups are decided by time and visual differences. In the indexing, SVMs are trained with features of pre-indexed faces to model target persons. The representative feature vector of the person group from the clustering is queried to the trained SVMs. Each SVM outputs a numeric confidence value about the query person group. The query person group is determined to the target person by the most confident SVM. The experimental results showed that the proposed method outperformed traditional person indexing method using only face feature and its performance increased to 93.56% from 72.31%.