Automatic detection and recognition of Indian currency note has gained a lot of research attention in recent years particularly due to its vast potential applications. In this paper we introduce a new recognition method for Indian currency using computer vision. It is shown that Indian currencies can be classified based on a set of unique non discriminating features such as color, dimension and most importantly the Identification Mark (unique for each denomination) mentioned in RBI guidelines. Firstly the dominant color and the aspect ratio of the note are extracted. After this the segmentation of the portion of the note containing the unique I.D. Mark is done. From these segmented image, feature extraction is done using Fourier Descriptors. As each note has a unique shape as the I.D. Mark, the classification of these shapes is done with the help of Artificial Neural Network. After feature extraction, the denominations are recognized based on the developed algorithm. The success rate of the proposed system is 97% requiring a processing time of 2.52 seconds.

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 headshoulder 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 headshoulder 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.

Abstract-Driven by the significant advancements in technology and social issues such as security management, there is a strong need for Smart Surveillance System in our society today. One of the key features of a Smart Surveillance System is efficient human detection and counting such that the system can decide and label events on its own. In this paper we propose a new, novel and robust model: "The Omega Model", for detecting and counting human beings present in the scene. The proposed model employs a set of four distinct descriptors for identifying the unique features of the head, neck and shoulder regions of a person. This unique head-neck-shoulder signature given by the Omega Model exploits the challenges such as inter person variations in size and shape of people's head, neck and shoulder regions to achieve robust detection of human beings even under partial occlusion, dynamically changing background and varying illumination conditions. After experimentation we observe and analyze the influences of each of the four descriptors on the system performance and computation speed and conclude that a weight based decision making system produces the best results. Evaluation results on a number of images indicate the validation of our method in actual situation.

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Automatic currency recognition and authentication has become an impending challenge today particularly because of the prevailing fraudulent activities as it hampers our economy. According to the RBI report 435,607 fake notes has been detected in year 2010-2011 and the number is only increasing with technological advancements in the field of printing. Image processing techniques such as texture based, pattern or color based, character recognition etc using different operator or tools such as Prewitt or Sobel or Canny edge detector, ANN, heuristic analysis, SVM etc are commonly used for recognition and authentication of paper currency note. Despite several researches it still remains an open challenge. This paper intends to present an extensive survey of the recent technological trends in recognition and authentication of paper currency note while identifying the various challenges.