We propose a personality detection algorithm based on human perceptions of images and created a mobile social application named Date gram to test the efficiency of our algorithm and connect people who share similar image perceptions. We detect user's Big Five personality based on their perceptual opinions on several images. Users in Date gram are matched to play visual-sentiment games, through which their connections based on personality similarity are further strengthened. Datagram utilizes techniques of image sentiment detection to analyze user's personality. Given the collected personal sentiment perception data, we proposed personality-mapping algorithm to categorize users into 'Big Five' personality. After the Adjective-Noun-Pairs (ANPs) are extracted from each image, a personalized image-profile that consists of multiple pairs of ANPs and corresponding user decisions is generated. This profile is treated as the input of our algorithm, and a modified Latent Dirichlet Allocation (LDA) model is applied to extract personality-related features. 'Big Five' personality values are calculated by a word-personality mapping algorithm where the technique of Linguistic Inquiry and Word Count (LIWC) is used. To evaluate our proposed algorithm, we did user studies over visual sentiments as well as personality detections. The results demonstrate that sentimental opinions from different persons vary significantly towards the same image, people would prefer to connect with those sharing similar perceptions of images and similar personalities with them.