Hand Gesture Recognition Research Paper

Hand Gesture recognition using

Hidden Markov Models Arpit Sharma

D.A.V.I.E.T, Jalandhar [e-mail] Abstract— With recent technological advancements in the field of artificial intelligence, human gesture recognition has gained special interests in the fields of computer vision and human computer interaction. Gesture recognition is a difficult problem because of the spatiotemporal variations in subject location, size in the frame. Subject occlusion also hinders the process of image recognition. Similarly, human gestures are a pattern recognition problem. This paper proposes a method to recognize time – varying human gestures from continuous video streams. This uses hidden Markov model method for the human hand gesture recognition.

V. FEATURE EXTRACTION, VECTOR QUANTIZATION & RECOGNITION

The main goal of the feature extraction step is to simplify recognition by summarizing vast amounts of image data and then obtaining the unique properties that define the gesture’s individuality.

In the initial steps, the observation of a feature is transformed into a pattern and then the distinct components are called features. A classifier is needed in the next step. Primarily it divides the features space into parts such that each corresponds to a pattern class.

Vector quantization is a lossy data compression method based upon principle of block coding. Vector quantization aims at minimizing distortions over all samples. Vector quantization is one of the preferred methods to lower distortions than using scalar quantization at the same

Basically, we use the Viterbi algorithm to find the most likely sequence of states. All the gestures can be put together parallelly with the initial and the final state thus creating a recognition model. There is no transition from the initial state of gesture to the initial state of the recognition model. Ditto happens between the final state of gesture to the final state of the recognition model.

VI. CONCLUSION

This paper produces a model for the hand gesture recognition using hidden Markov model. Hidden Markov model were originally used for the speech recognition learning algorithms, results show that the HMM can be successfully applied to hand gesture recognition as well. The results can be further improved by using better gesture segmentation and image noise filtration. The proposed method can be used in developing various learning algorithms in the field of robotics, human-computer interaction etc.

VII. REFERENCES

[1] T.B. Moeslund and E. Granum, "A survey of computer vision based human motion

Capture”,

[2] Christopher M. Bishop, “Patten recognition and machine