Facial emotions of humans transfer non-verbal signals which have a dynamic role in interactive communication. Human-machine interface evolves according to facial expression recognition because both have a significant relationship. Psychology ethical science, and robotics are necessary applications of facial expression recognition. A lot of work has been done already on feature extraction, face detection, and the famous techniques used for expression recognition. Weighted distance is the basic method of this research. It is used for recognition of all basic human emotions, such as anger, happiness, disgust, fear, neutral, sadness, and surprise. For the extraction of weighted distance paths, a fast-marching algorithm is used, and the seed point is taken on the nose tip of the human face. Diverse number of paths have also been taken, and they have had an effect on facial expression recognition. Intensity variation is the main motivation to use Weighted Distance Transform. Because the facial intensity variations or facial curvatures of most human beings are different, the accuracy of final evaluation may be increased and achieved in a respective manner by applying it. JAFFE [Japanese Female Facial Expression) database is used, and it is composed of 213 facial images of 10 Japanese female models with all seven basic emotions. The dimensions of JAFFE database are 256x256, and all the images are frontal position view. Twenty points are labelled for the calculation of feature vectors. Different mathematical measures are calculated as a feature vector of this geometric representation. Diverse seed locations are also being taken during research. Total four seed locations have been taken, and dissimilar number of points have also been applied for achieving better grades in the final evaluation. In classification, KNN is used and it illustrates reasonable results. In the end, validation is done with famous techniques of facial expression recognition.