openbr簡介

OpenBR [3] is a framework for investigating new modalities, improving existing algorithms, interfacing with commercial systems, measuring recognition performance, and deploying automated biometric systems. The project is designed to facilitate rapid algorithm prototyping, and features a mature core framework, flexible plugin system, and support for open and closed source development. Off-the-shelf algorithms are also available for specific modalities including Face RecognitionAge Estimation, and Gender Estimation.

OpenBR originated within The MITRE Corporation from a need to streamline the process of prototyping new algorithms. The project was later published as open source software under the Apache 2 license and is free for academic and commercial use.

Openbr是一個框架,用於調研新模型、提升已有算法、和商業模型組合,衡量識別以及構建自動化的生物識別系統。這個項目用於快速迭代算法模型、構建一個成熟的核心架構、靈活插件系統、支持開源以及封閉的源代碼。現成的算法也同樣適用於這套框架,包括人臉識別、年齡預估和姿態判斷。

Openbr起源於MTTRE組織,一個簡化原型設計的新算法的組織。這個項目後來在開源軟件公佈,並且免費用於學術和商業。


abstraction.svg
The two principal software artifacts are the shared library 'openbr' and command line application 'br'.


1.架構 
(1)數據結構
在openbr中有兩個重要的數據結構和6個接口,數據結構(1)文件結構體:存儲文件路徑和存儲關聯元數據的KV表;(2)計量生物結構體:opencv矩陣和關聯文件。
(2)插件
在openBR中使用插件是添加新算法的首選方式。
總共有6個抽象接口:實現新文件格式化:Format 、Gallery、Output;實現新的特徵計算和比對:Transform、Distance;初始化接口:Initializer。
A Format represents a template on disk either before or after enrollment. For example, images, videos, MATLAB
matricies, and many other extensions can be interpreted by format plugins.
A Gallery represents a template list on disk either be fore or after enrollment. The NIST .xml signature set and OpenBR binary .gal are the standard plugins for storing template lists before and after enrollment, though many others exist including Weka .arff.
An Output represents the result of comparing two galleries. The NIST .mtx binary similarity matrix is the preferred output,though many others exist including .rr rank retrieval and .csv plain text score matrix.
A Transform is a single step in a template generation algorithm, it applies the same image processing or numerical analysis algorithm to every template it receives. Transforms can be either trainable (e.g., LDA) or untrainable (e.g.,LBP). Time-varying transforms also exist to support objecttracking in video.
A Distance is capable of comparing two templates and returning a similarity score. OpenBR supports many common similarity metrics including norm-based, cosine, Chi-squared, and Bhattacharyya. Section 4.5 discusses a
particular distance metric novel to OpenBR.
Commercial algorithms can also be added to OpenBR by wrapping them in Transform and Distance plugins. To date, six commercial systems have been leveraged through the OpenBR API.


2.人臉識別
(1)人臉檢測
openBR對opencv的VJ對象識別方法進行了封裝,同時提供了基於casecade的正臉識別方法。對於人眼識別,提供了以及基於c++、接口爲ASEE的人眼識別方法ASEEEyes。
(2)規範化
人臉基於人臉位置做旋轉扭正。
(3)特徵描述
基於人臉網格提取LBP和SIFT特徵,LBP爲基於6*6的窗口每次計算8*8像素,SIF在12像素的半徑範圍內提取10*10的元素,PCA對每個局域特徵進行壓縮,生成最終的特徵空間,並進行格式化。
(4)特徵提取
提取12維特徵,每一維是總特徵的5%,然後使用LDA方法學習空間特徵。
(5)特徵比對匹配
計算所有特徵間的最大值和最小值

(6)最終算法


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