  Shape based classification is one of the most challenging tasks in the field
of computer vision. Shapes play a vital role in object recognition. The basic
shapes in an image can occur in varying scale, position and orientation. And
specially when detecting human, the task becomes more challenging owing to the
largely varying size, shape, posture and clothing of human. So, in our work we
detect human, based on the head-shoulder shape as it is the most unvarying part
of human body. Here, firstly a new and a novel equation named as the Omega
Equation that describes the shape of human head-shoulder is developed and based
on this equation, a classifier is designed particularly for detecting human
presence in a scene. The classifier detects human by analyzing some of the
discriminative features of the values of the parameters obtained from the Omega
equation. The proposed method has been tested on a variety of shape dataset
taking into consideration the complexities of human head-shoulder shape. In all
the experiments the proposed method demonstrated satisfactory results.
