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Fixed-point type human face detection method

A detection method, face detection technology, applied in the field of pattern recognition, can solve problems affecting the real-time performance of face detection algorithms, affecting the use of fixed-point embedded devices, etc.

Active Publication Date: 2012-11-14
BEIJING HANBANG GAOKE DIGITAL TECH
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Problems solved by technology

[0003] The AdaBoost algorithm is a fast algorithm for many face detection algorithms, but since most of the training model parameters in the algorithm are floating-point numbers, the calculation process is also a floating-point calculation, which affects the real-time performance of the face detection algorithm to a certain extent , also affects the use of fixed-point embedded devices

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  • Fixed-point type human face detection method
  • Fixed-point type human face detection method
  • Fixed-point type human face detection method

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specific Embodiment

[0098] Provide a specific embodiment below, comprise the following steps:

[0099] First, classify the relevant parameters of the AdaBoost face detection algorithm

[0100] Classify the parameters of the strong and weak classifiers according to the calculation characteristics of the classifier according to the content of the above part (a), and independently perform the floating-point number conversion of the floating-point parameters to the fixed-point number processing. Therefore, the relevant parameters of the strong classifier are: a t and a_th; weak classifier parameters are: θ, p; related calculation amount: s; in the algorithm implementation program, the related parameters are defined as follows:

[0101] Weak classifier:

[0102] long f1_int: Harr eigenvalue

[0103] unsigned long ex_int: integral map value me

[0104] unsigned long sq_int: square integral map sq

[0105] long sq_long_sqrt: normalized root mean square

[0106] long s_thresh: Weak classifier judgm...

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Abstract

The invention discloses a fixed-point type human face detection method. According to the method, fixed-point number conversion can be realized for parameters of a relevant training floating-point model of a waterfall cascaded classifier in an AdaBoost human face detection algorithm, and fixed-point calculation and conversion can be performed on a relevant floating-point calculation process. The method specifically comprises the steps of: (1) effective separation of relevant parameters of a strong classifier from relevant parameters of a weak classifier from the calibration perspective according to classified calculation characteristics of the strong classifier and the weak classifier in the waterfall cascaded classifier; (2) conversion of the floating-point number of the parameter theta into the fixed-point number, calculation of Harr features and fixed-point calculation of integral image calculation according to the classified calculation characteristics of the weak classifier; (3) conversion of the floating-point number of at and a_th in the strong classifier into the fixed-point number according to the classified calculation characteristics of the strong classifier and parameter definitions; and (4) conversion of floating-point calculation of the AdaBoost human face detection algorithm into fixed-point calculation.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a detection method of a fixed-point human face, that is, a detection and discrimination method for a straight-faced human face. Background technique [0002] Face detection technology has always been a research hotspot in the fields of pattern recognition and computer vision, and has a very broad application prospect. In the field of face detection research, with the AdaBoost algorithm proposed by Paul Viola as a milestone, face detection technology has become practical. Compared with previous face detection algorithms, AdaBoost algorithm has higher detection accuracy and rapidity, and has become Algorithm of choice for real-time systems. [0003] The AdaBoost algorithm is a fast algorithm for many face detection algorithms, but since most of the training model parameters in the algorithm are floating-point numbers, the calculation process is also a float...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 丘江杨慧松张海峰杨晔艾奇
Owner BEIJING HANBANG GAOKE DIGITAL TECH
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