Face detection and image acquisition

　　Face recognition system analysis: by the front end face capture system acquisition subsystem, network transmission and back-end subsystem management subsystems resolve to achieve the collection of traffic information face, transmission, processing, analysis and centralized management. System, the front face, head collection devices collect face images, access server main receiving and forwarding information and pictures, can be a variety of models, a number of manufacturers of machine snapshot provides a unified access service, received snapped pictures stored in the cloud storage unit by facial structure analysis server to capture video and image modeling and real-time blacklist than alarm, face information model and data modeling to get the data into large units. Backend analytic application platform is based on the application needs of users, supports real-time facial capture, retrieval and other functions, can provide real-time blacklist database than the snapshot picture of information to the user, provide fast and efficient service to found a suspicious target.
　　
　　Face detection and image capture
　　
　　facial image acquisition: different face images can be collected down through the camera lens, such as aspects of still images, moving images, different positions, different facial expressions, etc. can be a good acquisition. When the user who within the shooting range of the collection device, the device will automatically search for and collect the user's face captured image.
　　
　　Face Detection: face detection in practice is mainly used for face recognition preprocessing, i.e. to calibrate the exact position and size of the face in the image. Mode feature face image contained very rich, such as histogram features, color characteristics, template feature, structure and Haar features and so on. Face detection is useful to pick out where this information and use these features for face detection.
　　
　　The mainstream of face detection method using Adaboost learning algorithm based on the above features, Adaboost algorithm is a classification method is used, it is some weak classification together, the combination of strong new classification.
　　
　　Face detection process using the Adaboost algorithm selected some of the most representative feature rectangle face (weak classifiers), the manner of weighted voting weak classifiers configured as a strong classifier, and then a plurality of strong classifiers trained obtained series to form a cascade structure laminated classifier effectively improve the detection speed of the classifier.
　　
　　Face image preprocessing
　　
　　Face image pre-processing: preprocessing the image is a face based on face detection results, the image processing service process and ultimately to the feature extraction. An original image acquisition system is limited due to various conditions and random interference, often can not be used directly, it must be gradation correction, noise filtering and other image pre-processing of the image at an early stage. For the face image, its pre-compensation process includes light face image, gradation conversion, histogram equalization, normalization, geometric correction, filtering, and sharpening.
　　
　　Face image feature extraction
　　
　　face image feature extraction: feature recognition system may be used usually divided into visual feature, wherein the pixel count, the image transform coefficients facial features, facial features, etc. algebraic image. Facial feature extraction is for certain facial features carried out. Facial feature extraction, also known as the face representation, it is the process of facial feature modeling. Facial feature extraction method summed up into two categories: one is based on the characterization of knowledge; the other is the algebraic features or characterization methods based on statistical learning.
　　
　　Mainly the shape of a face organ, and describe characteristics of the distance between them contributes to the characteristic data obtained by face knowledge-based characterization methods, which typically comprises a component characterized in Euclidean distance, and angle of curvature between the feature point, etc. . Face consists of eyes, nose, mouth, chin topical, local and geometric description of these structural relationship between them, can be an important feature of face recognition, the features referred to geometric features. Knowledge-based human face representation includes a method and template matching based on geometric features.
　　
　　apiVersion: Apps / V1
　　
　　kind: DaemonSet
　　
　　Metadata:
　　
　　name: Node-Exporter-daemonset
　　
　　namespace: Agent
　　
　　spec:
　　
　　Selector:
　　
　　matchLabels:
　　
　　App: Prometheus
　　
　　template:
　　
　　metadata:
　　
　　labels:
　　
　　app: prometheus
　　
　　spec:
　　
　　hostNetwork: true
　　
　　containers:
　　
　　- name: node-exporter
　　
　　image: prom/node-exporter
　　
　　imagePullPolicy: IfNotPresent
　　
　　command:
　　
　　- /bin/node_www.hnawesm.com exporter
　　
　　- --path.procfs
　　
　　- /host/proc
　　
　　- --path.sysfs
　　
　　- /host/sys
　　
　　- --collector.filesystem.ignored-mount-points
　　
　　- ^/(sys|proc|dev|host|etc)www.jintianxuesha.com($|/)
　　
　　volumeMounts:
　　
　　- name: proc
　　
　　mountPath: /host/proc
　　
　　- name: sys
　　
　　mountPath: /host/sys
　　
　　- name: root
　　
　　mountPath: /rootfs
　　
　　volumes:
　　
　　- name: proc
　　
　　hostPath:
　　
　　path: / proc
　　
　　- name: SYS
　　
　　hostPath:
　　
　　path: / SYS
　　
　　- name: the root
　　
　　hostPath:
　　
　　path: /
　　
　　copy the code
　　
　　here for the time being not tangled configuration contents, including Host network, the container start command and Volume, The presentations will be back.
　　
　　Also, create a resource by kubectl:
　　
　　kubectl the Apply -f the Node-exporter.yaml
　　
　　Then, kubectl View Pod distribution:
　　
　　　　As can be seen, our Prometheus Node Exporter successful deployment and are deployed in only two nodes Node A Pod copy.
　　
　　Two, Job
　　
　　2.1 About Job
　　
　　For ReplicaSet, Deployment, DaemonSet and other types of controllers, the number is expected to keep its hopes Pod and lasting run down, unless the user explicitly deleted, otherwise these objects has always existed, they say it can be said lasting service the task.
　　
　　For non-durable tasks, such as file compression, the task is completed, Pod need to end the run, no Ppod remain in the system, this time is necessary to use Job. Therefore it can be said, Job is a supplement to ReplicaSet, Deployment, DaemonSet and other persistent controller.
　　
　　2.2 Job creation and running
　　
　　Create Resource same way as before, still be created through the use of YAML configuration file, you need to specify apiVersioin: batch and kind: Job can:
　　
　　apiVersion: BATCH / v1
　　
　　kind: Job
　　
　　(1) First Job
　　
　　Here we are with a simple little Job as an example, look at a simple Job: Job when up and running only a Pod, after the operation of the entire Job Pod will be over soon.
　　
　　Copy the code
　　
　　apiVersion: BATCH / V1
　　
　　kind: the Job
　　
　　Metadata:
　　
　　name: Hello edc-www.xcdeyiju.com Job-Job-
　　
　　namespace: Jobs
　　
　　spec:
　　
　　Template:
　　
　　Metadata:
　　
　　Labels:
　　
　　App: Hello EDC-Job-Job-
　　
　　spec:
　　
　　Containers:
　　
　　- name: the Hello-the Job
　　
　　Image: busybox
　　
　　imagePullPolicy: IfNotPresent
　　
　　the Command: [ "echo" www.csyldl.com, www.yuchengyulegw.com "the Hello Edison's K8S the Job!"]
　　
　　restartPolicy: Never
　　
　　Copy the code
　　
　　to note here is that for Job, its restartPolicy OnFailure or only to Never, which is its difference from the other controllers (e.g., the controller also allows to Deployment Always). To perform this task Job is also very simple, is the output of passage "hello edison's k8s job!" To the end of its life.
　　
　　PS: This uses a mirror image of busybox, busybox is a software toolkit, inside integrated in the Linux hundreds of commonly used Linux commands and tools. If we only need a small Linux operating environment to run the command, busybox can use this image instead of pulling a CentOS mirror.
　　
　　Face image matching and recognition: feature template extracted face image feature data stored in the database searching and matching, by setting a threshold value, when the similarity exceeds the threshold value, then the output of the matching results obtained. Face recognition is the facial features to be identified have been compared with people face features template, face identity information to judge according to the degree of similarity. This process is divided into two categories: one is confirmed, the process is one image comparison, and the other is to identify, is the process many image matching comparison.