Gait is an emergent biometric aimed essentially to recognize people by the way they walk.Gait as a biometric can be seen as advantageous over other forms of biometric identification techniques

for it offers the possibility to identify people at a distance without any interaction or co-operation from the subject.This paper proposes a novel automatic gait recognition method

which extracts gait signature from legs of the subject.For each image sequence

background subtraction based on chromaticity distortion is used to segment moving objects.Boundary tracking algorithm is then used to find perimeter pixels in each processed binary image sequence.This paper makes use of Hough Transform to locally extract the lines which represent legs

and thus obtains inclination angles of upper legs and lower legs.The angles are then fitted to a fifth-order polynomial by least squares method.The polynomial curve is expressed by a Fourier series.The lower-dimensional gait signature vector

that is

the product of phase and magnitude

is derived from phase and magnitude spectra.Fisher Linear Classifier is used to validate the performance of the proposed algorithm on small database samples and the correct classification rate is 79.17%.The recognition rate is still good for these unideal outdoor image sequences.