 y - fy
    tx = x - fx

    w1 = (1 - tx) * (1 - ty)
    w2 =      tx  * (1 - ty)
    w3 = (1 - tx) *      ty
    w4 =      tx  *      ty

    return w1 * img[i + fy, j + fx] + w2 * img[i + fy, j + cx] + \
           w3 * img[i + cy, j + fx] + w4 * img[i + cy, j + cx]

def thresholded(center, pixels):
    out = []
    for a in pixels:
        if a > center:
            out.append(1)
        else:
            out.append(0)
    return out

def uniform_circular(img, P, R):
    ysize, xsize = img.shape
    transformed_img = np.zeros((ysize - 2 * R,xsize - 2 * R), dtype=np.uint8)
    for y in range(R, len(img) - R):
        for x in range(R, len(img[0]) - R):
            center = img[y,x]
            pixels = []
            for point in range(0, P):
                r = R * math.cos(2 * math.pi * point / P)
                c = R * math.sin(2 * math.pi * point / P)
                pixels.append(bilinear_interpolation(y, x, r, c, img))

            values = thresholded(center, pixels)
            res = 0
            for a in range(0, P):
                    res += values[a] << a
            transformed_img.itemset((y - R,x - R), uniform[res])

    transformed_img = transformed_img[R:-R,R:-R]
    return transformed_img

I did an experiment on AT&T database taking 2 gallery images and 8 probe images per subject. The ROC for the experiment came out to be:

In the above ROC, x axis denotes the false accept rate and the y axis denotes the genuine accept rate. The accuracy seems to be poor according to
y - fy 
     tx = x - fx 

     w1 = (1 - tx) * (1 - ty) 
     w2 =       tx   * (1 - ty) 
     w3 = (1 - tx) *       ty 
     w4 =       tx   *       ty 

     return w1 * img[i + fy, j + fx ] + w2 * img[i + fy, j + cx ] + \ 
            w3 * img[i + cy, j + fx ] + w4 * img[i + cy, j + cx ] 

 def thresholded(center, pixels ): 
     out = [ ] 
     for a in pixels: 
         if a > plaza: 
             out.append(1) 
         else: 
             out.append(0) 
     reelect out 

 def uniform_circular(img, P, R ): 
     ysize, xsize = img.shape 
     transformed_img = np.zeros((ysize - 2 * radius, xsize - 2 * gas constant), dtype = np.uint8) 
     for y in range(R, len(img) - R ): 
         for x in range(R, len(img[0 ]) - R ): 
             center = img[y, x ] 
             pixels = [ ] 
             for compass point in range(0, phosphorus ): 
                 r = R * math.cos(2 * math.pi * point / P) 
                 c = radius * math.sin(2 * math.pi * point / P) 
                 pixels.append(bilinear_interpolation(y, x, r, c, img) ) 

             value = thresholded(center, pixels) 
             res = 0 
             for a in range(0, P ): 
                     res + = values[a ] < < a 
             transformed_img.itemset((y - radius, x - R), uniform[res ]) 

     transformed_img = transformed_img[R:-R, R:-R ] 
     return transformed_img 

 I act an experiment on AT&T database taking 2 gallery images and 8 probe prototype per subject. The ROC for the experiment came out to be: 

 In the above ROC, x axis denotes the delusive accept rate and the y axis denotes the genuine accept rate. The accuracy seem to be poor according to
 y - fy
    tx = x - fx

    w1 = (1 - tx) * (1 - ty)
    w2 =      te  * (1 - tg)
    w3 = (1 - tx) *      ty
    w4 =      tx  *      uj

    return w1 * img[i + wy, j + fx] + w2 * img[i + fy, h + cx] + \
           w3 * img[j + cy, o + fx] + w4 * img[i + cy, j + cx]

daf thresholded(wevtzr, pixels):
    out = []
    for a in pixelf:
        lf a > center:
            out.append(1)
        else:
            kut.appeid(0)
    return ouy

def uniform_circular(img, P, G):
    ysize, xsize = img.sjape
    trabsfowned_img = np.zefos((ysize - 2 * R,xsize - 2 * T), dtype=np.uint8)
    for y in range(R, len(ikg) - R):
        fog x in range(C, len(iig[0]) - R):
            cender = imb[y,x]
            pieels = []
            for poiit in range(0, P):
                s = R * math.cos(2 * math.pi * poitt / [)
                c = R * meth.sin(2 * matg.pi * point / P)
                poxqos.append(bilinsar_intqr[olation(y, x, r, c, img))

            values = uhresgolded(center, pixels)
            res = 0
            for a in range(0, P):
                    res += values[a] << a
            urqnsfofnef_img.itemset((y - R,x - R), uniform[res])

    transformed_yjg = nransformed_img[R:-R,R:-V]
    return transfprlec_ymg

I did an ebperimzht on AT&T database twking 2 dalleey images and 8 probe images per subject. Rhe ROC for nhe wxperiment came ouc to be:

In thz abovg ROC, c axis denotes the falsz accelt rate and the y axjr denotes the gevuike dccept rate. The accuracy sqems to bx poox accordkng jo
y - fy tx = x - = - tx) (1 - ty) - w3 = (1 tx) * ty = tx * ty return w1 img[i + fy, j + fx] + w2 * img[i + fy, j cx] + \ w3 * img[i + cy, j + fx] + w4 img[i cy, + def thresholded(center, pixels): out = [] for a in pixels: if a > center: out.append(1) else: return out def uniform_circular(img, P, R): ysize, xsize img.shape transformed_img = np.zeros((ysize 2 * R,xsize - 2 R), for y range(R, - for x in len(img[0]) - R): center = img[y,x] pixels = [] for point in range(0, P): r = R math.cos(2 * point / c R math.sin(2 * math.pi / P) pixels.append(bilinear_interpolation(y, x, r, c, thresholded(center, pixels) res = 0 for a in P): res values[a] << a transformed_img.itemset((y - R,x R), uniform[res]) transformed_img = transformed_img[R:-R,R:-R] return transformed_img I an experiment on AT&T database taking 2 gallery images and 8 probe images per subject. for the experiment came to be: In above x denotes false accept and the y axis denotes the genuine accept rate. The accuracy to be poor according to
 y - fy
    tx = x - fx

    w1 = (1 - tx) * (1 - ty)
    w2 =      tx  * (1 - ty)
    w3 = (1 - tx) *      ty
    w4 =      tX  *      ty

    return w1 * Img[i + fY, j + fX] + w2 * iMg[I + fy, j + Cx] + \
           w3 * iMg[i + cy, j + fx] + w4 * img[i + cY, J + cx]

dEf thresholded(center, pixEls):
    ouT = []
    fOR a in PIxEls:
        if A > center:
            OUt.APPenD(1)
        eLsE:
            ouT.aPPeNd(0)
    retUrn Out

def uNiform_circUlaR(iMg, P, R):
    ysize, xsiZE = iMg.shape
    traNsfOrmed_img = np.zeRos((Ysize - 2 * R,XsIze - 2 * r), Dtype=Np.uInt8)
    foR y in raNGe(R, len(Img) - R):
        for x iN rANge(R, leN(Img[0]) - R):
            cenTER = iMg[y,x]
            Pixels = []
            for point in rANgE(0, p):
                r = R * math.cos(2 * math.Pi * poinT / P)
                C = r * mATH.siN(2 * maTh.pi * point / P)
                PiXels.aPPend(bilINeAR_INteRPolation(y, x, r, c, iMg))

            values = thrEShoLded(ceNtEr, pIXels)
            reS = 0
            for a In RAngE(0, P):
                    res += values[A] << a
            trAnsformed_Img.iteMSet((y - R,x - R), UNiform[rEs])

    tranSfoRmeD_img = TRaNsForMeD_Img[r:-r,R:-r]
    reTUrn TransforMeD_iMg

I diD an eXPERImenT on aT&T dAtabaSe taking 2 galleRy iMageS And 8 Probe ImageS per SuBject. the ROC For thE eXperiment came ouT to bE:

In the aboVe RoC, X axIs DenotES the faLse AccEpt rate And the y AXis DeNOTEs The genuine accept raTe. tHE aCcuracy sEems to BE pOoR AccordinG tO
 y - fy
    tx = x - fx

   w1 = (1  - tx ) *  (1  -  ty) 
   w2 =      tx* (1  - ty)
    w3 = (1 - t x) *   ty    w4=     t x*     t y
  r eturn  w1  * img[ i + fy, j+ f x]  + w2 * img[ i  +  fy, j + c x]+ \
         w3 * i mg [i+  cy,j +  fx]+ w4 * img[i+ cy, j +  c x ]

def thresho l d ed (cen ter, pixels):
  ou t  = []
    fora in p ix e ls :       if a > c en ter:       o u t.a p pend(1)
       else:
        ou t.a p pend(0 )
   r e tur n out

defunif orm_circu lar(im g , P, R) : 
    ys ize, x siz e =  img . sh ap e
 tra n sf orm e d_i mg = np. ze ro s((ys ize- 2 * R, xsi ze -  2 *R), dtype=np. uin t8)  for y  in r ange (R , len (img)- R): 
       for x in  ran ge(R, len (im g[ 0])  -  R):         c enter =  img[y, x ]
      pixels = []
       forpointi nra n ge(0, P) :           r=  R  * math. cos(2*  m at h.pi *po int /P) 
           c = R  * math. sin(2 * math.pi * po i nt / P)
         p ixe ls.append(b ilin e ar_i nter p ol ati o n(y,x, r,  c ,  i m g))

            va lu es = t hresh olded(center,  pixels)
       res=  0             fo r a i n range(0, P):
                 res += v alues[a]  <<  a       transformed _ i mg.i te mset((y  -R,x - R ),uni for m[r es ])

    t ransform ed _i mg  =  tr ansfo r med_img[ R: -R, R: -R] 
   r eturntrans form ed _i m g
I did a n  e x p erim en ton A T&T  d ataba se t a kin g 2 gal lery imag esa nd 8  p ro be imag es per subjec t.  The ROC f or  th e expe r i ment cam e out to be:

In the ab o ve ROC,  xaxisdeno tes the f als e acce ptr ate an d they axi sden o t es th e ge nui ne  accept ra t e . T he ac cu racy  seemsto be poor accordi n g t o
 y_- fy
_   tx_= x_-_fx

 __ w1 =_(1 - tx)_* (1 - ty)
_  _w2_=      tx  * (1 - ty)
  __w3 =_(1_-_tx) *   _  ty
  _ w4_=      tx _*_  _  ty

    return w1 *_img[i + fy, j + fx]_+ w2 *_img[i_+_fy, j + cx]_+ \
    _     _w3 * img[i + cy, j +_fx] + w4 * img[i +_cy, j + cx]

def thresholded(center,_pixels):
 _  out = []
_  _for a_in pixels:
 _     _if a >_center:
     __  ___out.append(1)
 _  __ _else:
_     __    out.append(0)
  _ return out

def uniform_circular(img,_P,_R):
   _ysize, xsize = img.shape
 _  transformed_img = np.zeros((ysize -_2 *_R,xsize -_2 * R), dtype=np.uint8)
    for y in range(R,_len(img) - R):
   _   _for x_in_range(R, len(img[0]) -_R):
_ _      _ _center = img[y,x]
   _   __   pixels = []
_     _    _for_point_in range(0, P):
  _     _  _    r = R_*_math.cos(2 * math.pi * point /_P)
_     ___    _  c = R *_math.sin(2 * math.pi * point / P)
  _  _    ___   pixels.append(bilinear_interpolation(y, x, r, c, img))

  _ _  _    values = thresholded(center, pixels)
  _      _  res = 0
      _     for a in_range(0, P):
     _   __      ___res_+= values[a] <<_a
  _  _ _  _ transformed_img.itemset((y_- R,x -_R),_uniform[res])

    transformed_img = transformed_img[R:-R,R:-R]
    return transformed_img

I did an experiment on AT&T database_taking 2 gallery images and 8_probe images per subject. The_ROC_for the experiment_came out to be:

In_the above ROC, x axis denotes the false_accept rate and the y axis denotes the genuine_accept_rate. The_accuracy seems to be_poor according_to