新的人脸识别特征集_A New Feature Set for Face Detection
face_recognition人脸识别模块
face_recognition⼈脸识别模块face_recognition ⼈脸识别模块（安装之前，必须先安装dlib库）1. 基于dlib库，进⾏了⼆次封装。
号称是世界上最简洁的⼈脸识别库。
2. 训练数据集：是⿇省理⼯⼤学下属的学院利⽤13000多张照⽚为基础。
主要是以欧美⼈为主。
在 command line下安装模块时：F:\Python_AI\venv\Scripts> pip install face_recoginitonload_image_file : 加载要识别的⼈脸图像，返回Numpy数组，返回的是图⽚像素的特征向量（⼤⼩，⽅向）face_loctions: 定位图中⼈脸的坐标位置。
返回矩形框的location（top,right,bottom,left）face_landmarks: 识别⼈脸的特征点; 返回68个特征点的坐标位置（chin....）face_encodings: 获取图像中所有⼈脸的编码信息（向量）compare_faces: 由⾯部编码信息进⾏⾯部识别匹配。
#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionfrom PIL import Image, ImageDrawimport cv2# face_image = face_recognition.load_image_file('imgs/twis001.jpg')face_image = cv2.imread('imgs/twis001.jpg')face_marks_list = face_recognition.face_landmarks(face_image)pil_image = Image.fromarray(face_image)d = ImageDraw.Draw(pil_image)for face_marks in face_marks_list:facial_features = ['chin','left_eyebrow','right_eyebrow','nose_bridge','nose_tip','left_eye','right_eye','bottom_lip']for facial_feature in facial_features:# print("{}: 特征位置：{}".format(facial_feature, face_marks[facial_feature]))# d.line(face_marks[facial_feature], fill=(0, 255, 0), width=5)# d.point(face_marks[facial_feature], fill=(255, 0, 0))for p in face_marks[facial_feature]:print(p)cv2.circle(face_image, p, 1, (0, 255, 0), 1)cv2.imshow("images", face_image)cv2.imwrite("twis01.jpg",face_image)cv2.waitKey(0)cv2.destroyAllWindows()# pil_image.show()#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionfrom PIL import Image, ImageDraw, ImageFontimage_known = face_recognition.load_image_file('imgs/yangmi/yangmidanr00.jpg')image_unknown = face_recognition.load_image_file('imgs/yangmi/yangmihe02.jpg')image_known_encodings = face_recognition.face_encodings(image_known)[0]face_locations = face_recognition.face_locations(image_unknown)results = []for i in range(len(face_locations)):top, right, bottom, left = face_locations[i]face_image = image_unknown[top:bottom, left:right]face_encoding = face_recognition.face_encodings(face_image)if face_encoding:result = {}matches = face_pare_faces(face_encoding, image_known_encodings, tolerance=0.5) if True in matches:print('在未知图⽚中找到了已知⾯孔')result['face_encoding'] = face_encodingresult['is_view'] = Trueresult['location'] = face_locations[i]result['face_id'] = i + 1results.append(result)if result['is_view']:print("已知⾯孔匹配照⽚上的第{}张脸！".format(result['face_id']))pil_image = Image.fromarray(image_unknown)draw = ImageDraw.Draw(pil_image)view_face_locations = [i['location'] for i in results if i['is_view']]for location in view_face_locations:top, right, bottom, left = locationdraw.rectangle([(left, top), (right, bottom)], outline=(0, 255, 0), width=2)font = ImageFont.truetype("consola.ttf", 20, encoding='unic')draw.text((left, top - 20), "yangmi", (255, 0, 0), font=font)pil_image.show()# 可以试着⽤cv2来画框，和写字 puttext#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionimport cv2img_known = face_recognition.load_image_file("imgs/joedan/cows.jpeg")img_unkown = face_recognition.load_image_file("imgs/joedan/joedan01.jpg")face_encodings_known = face_recognition.face_encodings(img_known)face_encodings_unknow = face_recognition.face_encodings(img_unkown)[0]matches = face_pare_faces(face_encodings_known, face_encodings_unknow, tolerance=0.5) print(matches)locations = face_recognition.face_locations(img_known)print(locations)if True in matches:index = matches.index(True)match = locations[index]print(match)top, right, bottom, left = matchcv2.rectangle(img_known, (left, top), (right, bottom), (0, 0, 255), 2)cv2.imshow("images", img_known)cv2.waitKey(0)cv2.destroyAllWindows()。
人工智能基础(习题卷62)
人工智能基础(习题卷62)第1部分：单项选择题，共50题，每题只有一个正确答案,多选或少选均不得分。
1.[单选题]以下说话正确的是（）A)一个机器学习模型如果有较高准确率，总是说明这个分类器是好的B)如果增加模型复杂度，那么模型的测试错误率不一定会降低C)如果增加模型复杂度，那么模型的训练错误率总是会降低答案:C解析:一个机器学习模型如果有较高准确率，不能说明这个分类器是好的。
对于不平 衡的数据集进行预测时，正确率不能反映模型的性能。
模型越复杂，在训练集上越容易表现 好，在测试集上越容易表现不好。
2.[单选题]关于卷积层的说法，错误的是（）A)卷积核的尺寸是由人为指定的B)卷积核的参数值是人为指定的C)卷积层可以作为神经网络的隐藏层D)特征图是为卷积层的最终输出答案:B解析:3.[单选题]有两个样本点，第一个点为正样本，它的特征向量是（0, -1）；第二个点为负样本，它的特征向量是（2, 3）,从这两个样本点组成的训练集构建一个线性SVM 分类器的分类面方程是（）。
A)2x+_y=4B)x+2y=5C)x+2y=3D)2x-y=0答案:C解析:对于两个点来说，最大间隔就是垂直平分线，因此求出垂直平分线即可。
斜率是 两点连线的斜率的负倒数。
即-1/ （-1-3）/（0-2）=-1/2,可得戶-（l/2）x + C.过中点（0+2） /2, （-1+3）/2）= （1, 1）,可得 c=3/2,故方程为 x+2戶3。
4.[单选题]在具体求解中，能够利用与该问题有关的信息来简化搜索过程，称此类信息为（ ）A)启发信息B)简化信息C)搜索信息D)求解信息答案:A解析:5.[单选题]下列哪个不是RPA实施回报率的评估因素？（）A)成本节省B)生产力提升C)质量改进D)劳动力需求有规律答案:DA)人机交互系统B)机器人-环境交互系统C)驱动系统D)控制系统答案:A解析:7.[单选题]下面不属于人工智能研究基本内容的是()A)机器感知B)机器思维C)机器学习D)自动化答案:D解析:8.[单选题]大数据正快速发展为对数量巨大、来源分散、格式多样的数据进行采集、存储和关联分析，从中发现新知识、创造新价值、提升新能力的()A)新一代技术平台B)新一代信息技术和服务业态C)新一代服务业态D)新一代信息技术答案:B解析:9.[单选题]梯度下降算法中，损失函数曲面上轨迹最混乱的算法是以下哪种算法?A)SGDB)BGDC)MGDD)MBGD答案:A解析:10.[单选题]当不知道数据所带标签时，可以使用哪种技术促使带同类标签的数据与带其他标签的数据相分离？（）A)分类B)聚类C)关联分析D)隐马尔可夫链答案:B解析:11.[单选题]线性判别分析常被视为一种经典的（）技术。
人脸识别核心算法及MATLAB代码
人脸识别核心算法在检测到人脸并定位面部关键特征点之后，主要的人脸区域就可以被裁剪出来，经过预处理之后，馈入后端的识别算法。
识别算法要完成人脸特征的提取，并与库存的已知人脸进行比对，完成最终的分类。
我们在这方面的主要工作包括：∙基于LGBP的人脸识别方法问题：统计学习目前已经成为人脸识别领域的主流方法，但实践表明，基于统计学习的方法往往会存在“推广能力弱”的问题，尤其在待识别图像“属性”未知的情况下，更难以确定采用什么样的训练图像来训练人脸模型。
鉴于此，在对统计学习方法进行研究的同时，我们还考虑了非统计模式识别的一类方法。
思路：对于给定的人脸图像，LGBP方法首先将其与多个不同尺度和方向的Gabor滤波器卷积(卷积结果称为Gabor特征图谱)获得多分辨率的变换图像。
然后将每个Gabor特征图谱划分成若干互不相交的局部空间区域，对每个区域提取局部邻域像素的亮度变化模式，并在每个局部空间区域内提取这些变化模式的空间区域直方图，所有Gabor特征图谱的、所有区域的直方图串接为一高维特征直方图来编码人脸图像。
并通过直方图之间的相似度匹配技术（如直方图交运算）来实现最终的人脸识别。
在FERET四个人脸图像测试集合上与FERET97的结果对比情况见下表。
由此可见，该方法具有良好的识别性能。
而且LGBP方法具有计算速度快、无需大样本学习、推广能力强的优点。
参见ICCV2005表.LGBP方法与FERET'97最佳结果的对比情况∙基于AdaBoost的Gabor特征选择及判别分析方法问题：人脸描述是人脸识别的核心问题之一，人脸识别的研究实践表明：在人脸三维形状信息难以准确获取的条件下，从图像数据中提取多方向、多尺度的Gabor特征是一种合适的选择。
使用Gabor特征进行人脸识别的典型方法包括弹性图匹配方法（EGM）和Gabor特征判别分类法（GFC）。
EGM在实用中需要解决关键特征点的定位问题，而且其速度也很难提高；而GFC则直接对下采样的Gabor特征用PCA降维并进行判别分析，尽管这避免了精确定位关键特征点的难题，但下采样的特征维数仍然偏高，而且简单的下采样策略很可能遗漏了非常多的有用特征。
人脸识别介绍_IntroFaceDetectRecognition
Knowledge-based Methods: Summary
Pros:
Easy to come up with simple rules Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified Work well for face localization in uncluttered background
Template-Based Methods: Summary
Pros:
Simple
Cons:
Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledgebased methods)
Knowledge-Based Methods
Top Top-down approach: Represent a face using a set of human-coded rules Example:
The center part of face has uniform intensity values The difference between the average intensity values of the center part and the upper part is significant A face often appears with two eyes that are symmetric to each other, a nose and a mouth
人脸识别涉及到的知识
English Version:The Knowledge Behind Face Recognition TechnologyFace recognition is an advanced technology that has revolutionized the way we interact with digital systems. It involves a myriad of complex concepts and technologies, ranging from computer vision and artificial intelligence to biometric analysis. In this article, we will delve into the knowledge and principles behind face recognition.At its core, face recognition relies heavily on computer vision, a field that deals with the analysis and understanding of digital images and videos. Computer vision algorithms are trained to detect and identify patterns, such as edges, shapes, and textures, within these digital representations. In the context of face recognition, these algorithms are specifically designed to recognize and extract features from human faces.One of the most critical components of face recognition is feature extraction. This process involves identifying distinctive characteristics of a face, such as the shape of the eyes, nose, or mouth, and encoding them into a numerical representation. These features are then used to create a unique identifier for each individual, known as a faceprint.Faceprints are then compared against a database of pre-existing faceprints to identify a match. This comparison process is facilitated by algorithms that calculate the similarity between two faceprints based on various metrics. If a match is found, the system can then identify the individual associated with that faceprint.Artificial intelligence (AI) plays a crucial role in face recognition by enabling the system to learn and improve over time. Machine learning algorithms, such as deep neural networks, are trained on large datasets of faces to recognize patterns and make accurate identifications. As more data becomes available, these algorithms become more accurate and reliable.Biometric analysis is another important aspect of face recognition. Biometrics refers to the measurement and analysis of physical characteristics for identification purposes. In face recognition, biometric analysis involves measuring and comparing facial features to verify the identity of an individual.In conclusion, face recognition technology draws upon a vast array of knowledge and technologies, including computer vision, artificial intelligence, and biometric analysis. The complexity and sophistication of these components make face recognition a powerful and accurate tool for identification and verificationin various applications.Chinese Version:人脸识别背后的知识人脸识别技术已经彻底改变了我们与数字系统的交互方式，是一项革命性的技术。
人脸关键点检测 经典算法
人脸关键点检测经典算法人脸关键点检测经典算法是计算机视觉领域的一个重要研究方向，它旨在识别和定位人脸图像中的关键点，如眼睛、鼻子、嘴巴等。
本文将介绍人脸关键点检测的基本原理以及三种经典算法：传统机器学习方法、深度学习方法和级联回归方法。
通过分析比较这些算法的优劣势，我们能够更好地理解人脸关键点检测技术的发展和应用。
一、人脸关键点检测基本原理人脸关键点检测的基本原理是将人脸图像中的关键点位置信息映射到特定的坐标系中。
这样一来，我们就可以通过机器学习或深度学习算法来训练模型，使其能够自动识别和定位这些关键点。
具体来说，人脸关键点检测的基本步骤包括以下几个方面：1. 数据准备：从人脸图像或视频中收集一系列标注好的训练样本，其中包含了关键点的位置信息。
2. 特征提取：将人脸图像转换成计算机可以理解的特征向量。
常用的特征包括灰度直方图、梯度直方图和局部二值模式等。
3. 模型训练：使用机器学习或深度学习算法对提取的特征进行训练，以建立关键点检测模型。
4. 模型测试和优化：使用测试集评估模型的性能，并根据需要对模型进行调整和优化。
二、传统机器学习方法传统机器学习方法在人脸关键点检测中有着较长的历史。
常用的传统机器学习方法包括支持向量机(SVM)、随机森林(RandomForest)和神经网络等。
在传统机器学习方法中，特征提取是一个关键问题。
基于传统机器学习方法的人脸关键点检测通常使用手工设计的特征表示，如HOG(Histogram of Oriented Gradients)、SIFT(Scale-Invariant Feature Transform)和SURF(Speeded Up Robust Features)等。
其中，HOG是一种常用的特征表示方法，它通过计算图像中不同方向上梯度的直方图来描述图像的纹理和边缘信息。
SIFT和SURF 则是基于图像局部特征的表示方法，它们可以在尺度、旋转和光照变化下保持特征的稳定性。
人脸识别(英文)Face-Recognition
Application
Face Recognition Access Control System
Face Recognition access control system is called FaceGate, Whenever one wishes to access a building, FaceGate verifies the person’s entry code or card, then compares his face with its stored “key.” It registers him as being authorized and allows him to enter the building. Access is denied to anyone whose face does not match.
Fundamentals
step 3 ) recognization process
After step2, the extracted feature of the input face is matched against those faces in the database; just like this pictuer, it outputs the result when a match is found.
n A computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.
Processing Flow
Application
简要说明人脸识别技术的流程
简要说明人脸识别技术的流程英文回答：Facial recognition technology is a process that involves the identification and verification of individuals based on their facial features. The process can be divided into several steps.1. Face Detection: The first step in facial recognition is to detect and locate faces in an image or video. This involves using algorithms to identify facial features such as eyes, nose, and mouth.2. Face Alignment: Once the faces are detected, the next step is to align them in a standardized way. This is done to ensure that the facial features are in a consistent position for accurate analysis.3. Feature Extraction: In this step, unique features of the face are extracted to create a facial template. Thesefeatures can include the distance between the eyes, the shape of the nose, and the curvature of the lips. These templates are then used for comparison and identification purposes.4. Face Matching: The extracted facial templates are compared with those stored in a database. This is done by calculating the similarity between the features of the input face and the stored templates. If a match is found, the person is identified.5. Decision Making: Once a match is found, a decision is made based on the requirements of the application. This can include granting access to a secure area, unlocking a device, or triggering an alert if the person is a suspect.It is important to note that facial recognition technology is not 100% accurate and can be influenced by factors such as lighting conditions, facial expressions, and occlusions. However, advancements in deep learning and artificial intelligence have significantly improved the accuracy and reliability of facial recognition systems.中文回答：人脸识别技术是一种基于人脸特征进行个体识别和验证的过程。
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Type 1&2:
The red area ratio is only required to be [1/2 1) times the whole area The relative position of red area is not required to be in the middle of whole area. The relative position of red area is not required to be in the middle of white area. The width and height are not required to be the same, only even lengths are required.
TII type 2 (TII2) consists a right triangle with the right angle at the up-right corner. Let the width of the original image be W, TII2(1:W+1, -1) = 0, TII2(1:W+1, 0) = 0, (1, 1) TII2(0, y) = 0. TII2(x-1, y-2) TII2(x, y) = I (x, y) TII2(x-1, y-1) + TII2(x, y-1) I (x, y) + TII2(x-1, y-1) W TII2(x, y-1) - TII2(x-1, y-2).
Type 1
Type 2
Type 3
Type 4
MIR Lab, ISA/NTHU
Type 5
michael@.tw
Type 6
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3.2.1 Extension of Rectangle Features (Cont.)
Extensions of each type:
0 0 0 0 0 0 0 0 0 0 0
0
0
0
0
0
0
0
0
0
0
0
0 0 0
0
0 0 0 0 0 0
W
MIR Lab, ISA/NTHU michael@.tw
TII1(x+1, y-1)
9
3.1.2 Triangle Integral Images (Cont.)
MIR Lab, ISA/NTHU michael@.tw 4
2. Related Work
2001 – Viola and Jones introduce:
Rectangle
integral image Training algorithm based on AdaBoost A cascaded structure
g. RII(x2+w2-1, y2-1) a. RII(x1-1, y1-1) h. RII(x2-1, y2-1) (x1, y1) (x2, y2) h2 f. TII1(x2+w2, y2-1) b. RII(x1+w1-1, y1-1) h1 w2 w1 d. RII(x1+w1-1, y1+h1-1)
MIR Lab, ISA/NTHU
michael@.tw
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3.2 Features (Cont.)
A feature extracts a value by subtracting the sum of pixels of the light area from the sum of pixels of the dark area in an image. A sum of a rectangle area can be computed by four points in a RII:
2X
Three rectangle feature:
1
=
2
MIR Lab, ISA/NTHU
–2X
1
–2X
2
michael@.tw
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3.2.2 Type 7: Triangle Feature Type A
This research introduces two new types of triangle features. Up-left right triangle feature (type 7):
a. RII(x-1, y-1) (x, y) height d. RII(x+width-1, y+height-1) b. RII(x+width-1, y-1)
c. RII(x-1, y+height-1)
width
The sum of pixels in gray area = d - b - c + a .
Image Resize image Contrast stretching
Transform to integral images
Face Detection
Faces Non-faces
MIR Lab, ISA/NTHU
michael@.tw
3
Outline
1. Introduction 2. Related Work 3. Integral Image and Features 4. Learning Strong Classifiers 5. Cascade of Classifiers 6. Experimental Results 7. Conclusions and Future Work
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
MIR Lab, ISA/NTHU
michael@.tw
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3.2 Features
We can observe that a human face has some useful characteristics, for example:
I ( x' , y ' )
RII(x, y)
(x, y)
The RII at location (x, y) contains the sum of the pixels above and to the left of (x, y). We also calculate the square sum of the image calculating variance for contrast correction (see 6.1).
MIR Lab, ISA/NTHU
michael@.tw
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3.1.1 Rectangle Integral Image
The definition of Rectangle Integral Image (RII):
RII( x, y)
x ' x , y ' y
MIR Lab, ISA/NTHU michael@.tw 2
1. Introduction
Face detection is an important component of a video information retrieval system. This research focuses on the upright-frontal face detection problem. System flow chart:
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MIR Lab, ISA/NTHU
3.2.1 Extension of Rectangle Features
We extend some feature types to be more generalized. Totally six types of rectangle features are u3, 4&5:
Type 6:
MIR Lab, ISA/NTHU
3.2.1 Extension of Rectangle Features (Cont.)
We calculate rectangle features by subtracting areas of two (type 1~5) or three (type 6) rectangles. Two rectangle feature: = –
michael@.tw 8
MIR Lab, ISA/NTHU
3.1.2 Triangle Integral Images
This research introduces two new types of triangle integral images (TIIs). TII type 1 (TII1) consists a right triangle with the right angle at the up-left corner. TII1(x+1, y-2) Let the width of the original image be W, TII1(1:W+1, -1) = 0, (1, 1) TII1(1:W+1, 0) = 0, TII1(W+1, y) = 0. TII1(x, y-1) TII1(x, y) = I (x, y) + TII1(x, y-1) I (x, y) + TII1(x+1, y-1) - TII1(x+1, y-2).
The
area of eyes is usually darker than the area of the bridge of the nose. The area across eyes is usually darker than the area of cheeks. (proposed by Viola)