Human face recognition and feature extraction allow people to recognize a huge number of faces in a small amount of time, rather than recognizing each image and it’s features individually through a normal human’s eyes. Using these technologies researches are being carried out to find the characters that look like humans. The face detection and feature extraction methods for human images are hardly applied to the cartoon images because the features of cartoon characters differ from human features. This research was conducted to find the techniques to face detection, feature extraction of cartoon characters and recognize look-alike cartoon characters for a given human image. We have created a Disney cartoon repository including 800 images from 77 characters, including 35 labeled landmarks using imglab tool. For cartoon face detection and feature extraction, landmark based models are trained using our dataset. Total 92 features (50 areas and 42 distances between landmarks) are stored as csv files along with the cartoon images. To compare features of a real image with all the cartoon image features euclidean distance was considered. To increase the performance, we used the landmark based model with a hair extraction model and also included a gender prediction model. Alternatively, we implemented a classification model to find the best matching cartoon character. It shows 84% accuracy on training data and 80% accuracy on validation after 100 epochs. Evaluating the results of each model is done by rank based comparison. As this is the beginning of human-cartoon mapping, we were able to find the doppelganger cartoon for a human with considerable accuracy and our own created cartoon dataset will be more useful to future researches.