         renderContext.drawImage(outputImage, inRect: videoDisplayViewRect, fromRect: drawFrame)
         videoDisplayView.display()
         
+        // NSDictionary型のoptionを生成。顔認識の精度を追加する.
+        var options : NSDictionary = NSDictionary(object: CIDetectorAccuracyHigh, forKey: CIDetectorAccuracy)
+        
+        // CIDetectorを生成。顔認識をするのでTypeはCIDetectorTypeFace.
+        let detector : CIDetector = CIDetector(ofType: CIDetectorTypeFace, context: nil, options: options as [NSObject : AnyObject])
+        
+        // detectorで認識した顔のデータを入れておくNSArray.
+        var faces : NSArray = detector.featuresInImage(outputImage, options: options as [NSObject : AnyObject])
+        
+        // UIKitは画面左上に原点があるが、CoreImageは画面左下に原点があるのでそれを揃えなくてはならない.
+        // CoreImageとUIKitの原点を画面左上に統一する処理.
+        var transform : CGAffineTransform = CGAffineTransformMakeScale(1, -1)
+        transform = CGAffineTransformTranslate(transform, 0, -videoDisplayView.bounds.size.height)
+        
+        // 検出された顔のデータをCIFaceFeatureで処理.
+        var feature : CIFaceFeature = CIFaceFeature()
+        for feature in faces {
+            
+            // 座標変換.
+            let faceRect : CGRect = CGRectApplyAffineTransform(feature.bounds, transform)
+            
+            // 画像の顔の周りを線で囲うUIViewを生成.
+            var faceOutline = UIView(frame: faceRect)
+            faceOutline.layer.borderWidth = 1
+            faceOutline.layer.borderColor = UIColor.redColor().CGColor
+            videoDisplayView.addSubview(faceOutline)
+            
+        }
+        
     }
     
     override func didReceiveMemoryWarning() {

        // Dispose of any resources that can be recreated.
    }
    
    
/*    func detectFaces() {

/*        let myImage : UIImage = UIImage.ResizeÜIImage(UIImage(named: "sample")!, width: self.view.frame.width, height: self.view.frame.height)
    
        // UIImageViewの生成.
        let myImageView : UIImageView = UIImageView()
        myImageView.frame = CGRectMake(0, 0, myImage.size.width, myImage.size.height)
        myImageView.image = myImage
        self.view.addSubview(myImageView)
*/
        
        // NSDictionary型のoptionを生成。顔認識の精度を追加する.
        var options : NSDictionary = NSDictionary(object: CIDetectorAccuracyHigh, forKey: CIDetectorAccuracy)
    
        // CIDetectorを生成。顔認識をするのでTypeはCIDetectorTypeFace.
        let detector : CIDetector = CIDetector(ofType: CIDetectorTypeFace, context: nil, options: options as [NSObject : AnyObject])
    
        // detectorで認識した顔のデータを入れておくNSArray.
        var faces : NSArray = detector.featuresInImage(CIImage(image: outputImage)!, options: options)
    
        // UIKitは画面左上に原点があるが、CoreImageは画面左下に原点があるのでそれを揃えなくてはならない.
        // CoreImageとUIKitの原点を画面左上に統一する処理.
        var transform : CGAffineTransform = CGAffineTransformMakeScale(1, -1)
        transform = CGAffineTransformTranslate(transform, 0, -myImageView.bounds.size.height)
    
        // 検出された顔のデータをCIFaceFeatureで処理.
        var feature : CIFaceFeature = CIFaceFeature()
        for feature in faces {
    
        // 座標変換.
        let faceRect : CGRect = CGRectApplyAffineTransform(feature.bounds, transform)
    
        // 画像の顔の周りを線で囲うUIViewを生成.
        var faceOutline = UIView(frame: faceRect)
        faceOutline.layer.borderWidth = 1
        faceOutline.layer.borderColor = UIColor.redColor().CGColor
        myImageView.addSubview(faceOutline)
            
        }
    }
*/
    
    
}
        // カメラの使用準備.
        self.initCamera()
        
    }
    
    override func viewDidDisappear(animated: Bool) {
